CN117036376B - Lesion image segmentation method, device and storage medium based on artificial intelligence - Google Patents
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Abstract
Description
技术领域Technical field
本申请涉及但不限于图像处理、人工智能技术领域,尤其涉及一种基于人工智能的病变图像分割方法、装置及存储介质。The present application relates to, but is not limited to, the technical fields of image processing and artificial intelligence, and in particular, to a method, device and storage medium for segmenting lesion images based on artificial intelligence.
背景技术Background technique
病灶识别是医学影像分析的重要任务之一,旨在从医学图像中准确地定位和提取出感兴趣的异常区域或病灶。其中,需要借助多种图像处理技术进行辅助协同,例如图像增强、图像分割、特征提取、特征选择、分类器构建、对象定位跟踪等等。在医学图像分割中,通过对医学图像,尤其是病变图像进行感兴趣区域地提取分离,有助于后续基于分割结果进行病灶识别定位。常见的分割方式仅基于阈值分割、边缘检测、区域生长、水平线分割等方式进行,对于后续的病灶识别的帮助有限。此外,现有的病变图像分割时,受限于人工标记的局限,分割的准确性和效率还有一定的提升空间。Lesion identification is one of the important tasks in medical image analysis, which aims to accurately locate and extract abnormal areas or lesions of interest from medical images. Among them, a variety of image processing technologies are needed for auxiliary collaboration, such as image enhancement, image segmentation, feature extraction, feature selection, classifier construction, object positioning and tracking, etc. In medical image segmentation, by extracting and separating areas of interest in medical images, especially lesion images, it is helpful to subsequently identify and locate lesions based on the segmentation results. Common segmentation methods are only based on threshold segmentation, edge detection, region growing, horizontal line segmentation, etc., which are of limited help for subsequent lesion identification. In addition, existing lesion image segmentation is limited by the limitations of manual labeling, and there is still room for improvement in segmentation accuracy and efficiency.
发明内容Contents of the invention
有鉴于此,本申请实施例至少提供一种基于人工智能的病变图像分割方法、装置及存储介质。In view of this, embodiments of the present application at least provide an artificial intelligence-based lesion image segmentation method, device, and storage medium.
本申请实施例的技术方案是这样实现的:The technical solution of the embodiment of this application is implemented as follows:
第一方面,本申请提供一种基于人工智能的病变图像分割方法,所述方法包括:In the first aspect, this application provides an artificial intelligence-based lesion image segmentation method, which method includes:
获取待分割医学影像图像,对所述待分割医学影像图像进行图像分块操作,得到所述待分割医学影像图像对应的待识别图像分块集合,其中,所述待分割医学影像图像通过体绘制得到;Obtain the medical image image to be segmented, perform an image segmentation operation on the medical image image to be segmented, and obtain a set of image segments to be identified corresponding to the medical image image to be segmented, wherein the medical image image to be segmented is rendered through volume rendering get;
确定所述待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过所述各个待识别医学图像分块的病理辅助促进指标从所述待识别图像分块集合中确定得到参照图像分块集合;所述参照图像分块集合中的医学图像分块是所述待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块;Determine the pathological auxiliary promotion index of each to-be-identified medical image block in the to-be-identified image block set, and use the pathological auxiliary promotion index of each to-be-identified medical image block from the to-be-identified image block set. Determine to obtain a reference image block set; the medical image blocks in the reference image block set are to-be-identified medical image blocks whose corresponding pathological auxiliary promotion indicators in the to-be-identified image block set satisfy the identification conditions;
根据所述参照图像分块集合确定识别图像分块集合,所述识别图像分块集合中的识别图像分块为可疑图像分块;Determine a recognition image segment set according to the reference image segment set, and the recognition image segment in the recognition image segment set is a suspicious image segment;
根据所述识别图像分块集合进行识别图像分块识别,以及在根据识别结果深度识别出满足标记临界条件时进行图像分块标记;Perform recognition image segmentation recognition according to the recognition image segmentation set, and perform image segmentation marking when it is determined based on the depth of the recognition result that the critical condition for marking is met;
通过每个所述待识别医学图像分块的图像分块标记结果对所述待分割医学影像图像进行分割。The medical image image to be segmented is segmented based on the image segment labeling result of each medical image segment to be identified.
在一些实施例中,所述确定所述待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,包括:In some embodiments, determining the pathological auxiliary promotion index of each to-be-identified medical image block in the set of image blocks to be identified includes:
针对所述待识别图像分块集合中分块尺寸大于尺寸阈值的任意一个第一待识别医学图像分块,确定第一待识别医学图像分块的内关联性评价指标和第一待识别医学图像分块的外关联性评价指标,以及基于第一待识别医学图像分块的内关联性评价指标和第一待识别医学图像分块的外关联性评价指标确定第一待识别医学图像分块的病理辅助促进指标;For any first medical image block to be identified in the set of image blocks to be identified whose block size is greater than a size threshold, determine the internal correlation evaluation index of the first medical image block to be identified and the first medical image to be identified The external correlation evaluation index of the block, and the determination of the first medical image block to be identified based on the internal correlation evaluation index of the first medical image block to be identified and the external correlation evaluation index of the first medical image block to be identified. Pathological auxiliary promotion indicators;
针对所述待识别图像分块集合中分块尺寸小于或等于所述尺寸阈值的任意一个第二待识别医学图像分块,确定第二待识别医学图像分块的外关联性评价指标,并将第二待识别医学图像分块的外关联性评价指标作为第二待识别医学图像分块的病理辅助促进指标。For any second medical image block to be identified in the set of image blocks to be identified whose block size is less than or equal to the size threshold, determine the external correlation evaluation index of the second medical image block to be identified, and set The external correlation evaluation index of the second medical image block to be identified is used as the pathological auxiliary promotion index of the second medical image block to be identified.
在一些实施例中,所述待分割医学影像图像为目标三维医学影像数据中的任意一个切片;所述确定第一待识别医学图像分块的内关联性评价指标,包括:In some embodiments, the medical image image to be segmented is any slice in the target three-dimensional medical image data; and determining the internal correlation evaluation index of the first medical image segment to be identified includes:
确定所述第一待识别医学图像分块对应的像素团分组,任意一个像素团分组通过对所述第一待识别医学图像分块进行切割得到的多个切割像素团组成,每个切割像素团由所述第一待识别医学图像分块中的一个像素或多个环绕像素组成;Determine the pixel cluster group corresponding to the first medical image segment to be identified. Any pixel cluster group is composed of multiple cut pixel clusters obtained by cutting the first medical image segment to be identified. Each cut pixel cluster is Composed of one pixel or multiple surrounding pixels in the first medical image block to be identified;
针对任意一个像素团分组,获取像素团分组中的每个切割像素团在所述目标三维医学影像数据中出现的置信度;For any pixel cluster group, obtain the confidence that each cut pixel cluster in the pixel cluster group appears in the target three-dimensional medical imaging data;
根据所述每个切割像素团在所述目标三维医学影像数据中出现的置信度以及所述第一待识别医学图像分块在所述目标三维医学影像数据中出现的置信度,确定像素团分组所对应的内关联性评价指标;According to the confidence that each cut pixel cluster appears in the target three-dimensional medical imaging data and the confidence that the first medical image block to be identified appears in the target three-dimensional medical imaging data, determine the pixel cluster grouping The corresponding internal correlation evaluation index;
将各个像素团分组所对应的内关联性评价指标中的最小内关联性评价指标确定为所述第一待识别医学图像分块的内关联性评价指标;Determine the minimum internal correlation evaluation index among the internal correlation evaluation indicators corresponding to each pixel group grouping as the internal correlation evaluation index of the first to-be-identified medical image block;
所述确定第一待识别医学图像分块的外关联性评价指标,包括:Determining the external correlation evaluation index of the first medical image block to be identified includes:
在所述目标三维医学影像数据中确定所述第一待识别医学图像分块的邻域像素集合,所述邻域像素集合包括不少于一个邻域像素;Determine a neighborhood pixel set of the first to-be-identified medical image block in the target three-dimensional medical image data, where the neighborhood pixel set includes no less than one neighborhood pixel;
获取每个邻域像素与所述第一待识别医学图像分块组合得到的待识别融合医学图像分块分别在所述目标三维医学影像数据中出现的置信度;Obtain the confidence that the fused medical image blocks to be identified, obtained by combining each neighborhood pixel and the first medical image block to be identified, respectively appear in the target three-dimensional medical image data;
通过每个待识别融合医学图像分块在所述目标三维医学影像数据中出现的置信度,确定所述第一待识别医学图像分块的外关联性评价指标。The external correlation evaluation index of the first medical image segment to be identified is determined based on the confidence of each fused medical image segment to be identified appearing in the target three-dimensional medical image data.
在一些实施例中,所述邻域像素集合包括的邻域像素为所述第一待识别医学图像分块在所述目标三维医学影像数据中的环绕像素;所述获取每个邻域像素与所述第一待识别医学图像分块组合得到的待识别融合医学图像分块分别在所述目标三维医学影像数据中出现的置信度,包括获取所述邻域像素集合中的每个环绕像素与所述第一待识别医学图像分块组合得到的第一待识别融合医学图像分块分别在所述目标三维医学影像数据中出现的置信度;In some embodiments, the neighborhood pixels included in the neighborhood pixel set are surrounding pixels of the first to-be-identified medical image block in the target three-dimensional medical image data; the acquisition of each neighborhood pixel and The confidence that the fused medical image blocks to be identified respectively appear in the target three-dimensional medical image data obtained by combining the first medical image blocks to be identified includes obtaining each surrounding pixel in the neighborhood pixel set and The confidence that the first fused medical image blocks to be identified obtained by combining the first medical image blocks to be identified appear in the target three-dimensional medical image data respectively;
所述通过每个待识别融合医学图像分块在所述目标三维医学影像数据中出现的置信度,确定所述第一待识别医学图像分块的外关联性评价指标,包括:Determining the external correlation evaluation index of the first medical image segment to be identified based on the confidence that each fused medical image segment to be identified appears in the target three-dimensional medical image data includes:
通过每个第一待识别融合医学图像分块在所述目标三维医学影像数据中出现的置信度获取多个第一不确定性度量值;Obtain a plurality of first uncertainty measurement values based on the confidence of each first to-be-identified fused medical image segment appearing in the target three-dimensional medical image data;
将所述多个第一不确定性度量值中的最小不确定性度量值确定为所述第一待识别医学图像分块的外关联性评价指标。The minimum uncertainty metric value among the plurality of first uncertainty metric values is determined as the external correlation evaluation index of the first to-be-identified medical image block.
在一些实施例中,所述待分割医学影像图像为目标三维医学影像数据中的任意一个切片,所述目标三维医学影像数据是在当前获取时刻获取得到;所述根据所述参照图像分块集合确定识别图像分块集合,包括:In some embodiments, the medical image image to be segmented is any slice in the target three-dimensional medical image data, and the target three-dimensional medical image data is obtained at the current acquisition moment; the block set is based on the reference image Determine the set of recognized image blocks, including:
获取参考病变图像集,所述参考病变图像集为通过正常图像分块以及根据在所述当前获取时刻之前获取的三维医学影像数据确定的识别图像分块组建的;Acquire a reference lesion image set, the reference lesion image set being composed of normal image segments and identification image segments determined based on three-dimensional medical imaging data acquired before the current acquisition moment;
在所述参考病变图像集中对所述参照图像分块集合中的每个医学图像分块进行匹配,基于匹配不成功的医学图像分块确定所述识别图像分块集合。Each medical image block in the reference image block set is matched in the reference lesion image set, and the identification image block set is determined based on the unsuccessfully matched medical image block.
在一些实施例中,所述目标识别图像分块为所述识别图像分块集合中的任意一个识别图像分块,所述方法还包括:In some embodiments, the target recognition image segment is any recognition image segment in the recognition image segment set, and the method further includes:
在所述参考病变图像集中匹配与所述目标识别图像分块匹配的匹配医学图像分块;如果匹配到,则通过所述目标识别图像分块和所述匹配医学图像分块确定新增识别图像分块,以及基于所述新增识别图像分块对所述识别图像分块集合进行迭代;基于迭代后的识别图像分块集合对所述参考病变图像集进行迭代。Match the matching medical image segment that matches the target recognition image segment in the reference lesion image set; if matched, determine the new recognition image through the target recognition image segment and the matching medical image segment. block, and iterating the set of recognition image blocks based on the newly added recognition image blocks; and iterating the set of reference lesion images based on the iterated set of recognition image blocks.
在一些实施例中,目标识别图像分块为所述识别图像分块集合中的任意一个识别图像分块,所述根据所述识别图像分块集合进行识别图像分块识别,包括:In some embodiments, the target recognition image block is any recognition image block in the recognition image block set, and the recognition image block recognition based on the recognition image block set includes:
获取所述目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,以及获取所述目标识别图像分块在目标获取时刻获取得到三维医学影像数据中的第二出现频次,所述目标获取时刻为S个获取时刻中除所述R个获取时刻之后的获取时刻;计算所述第一出现频次与所述第二出现频次之间的商;将所述第一出现频次与所述第二出现频次之间的商确定为所述目标识别图像分块的识别结果;Obtaining the first occurrence frequency of the target recognition image segment in the three-dimensional medical imaging data at R acquisition times, and acquiring the second occurrence frequency of the target recognition image segment in the three-dimensional medical imaging data at the target acquisition time frequency, the target acquisition time is the acquisition time after the R acquisition moments among the S acquisition times; calculate the quotient between the first occurrence frequency and the second occurrence frequency; divide the first occurrence frequency into The quotient between the frequency and the second frequency of occurrence is determined as the recognition result of the target recognition image block;
或者,所述根据所述识别图像分块集合进行识别图像分块识别,包括:Alternatively, the step of performing recognition image segment recognition based on the recognition image segment set includes:
获取所述目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,以及获取所述目标识别图像分块在S个获取时刻获取得到三维医学影像数据中的第三出现频次;Obtaining the first frequency of occurrence of the target recognition image segment in the three-dimensional medical imaging data at R acquisition times, and acquiring the third frequency of the three-dimensional medical imaging data in the S acquisition time of the target recognition image segment frequency of occurrence;
计算所述第一出现频次与所述第三出现频次之间的商;Calculate the quotient between the first frequency of occurrence and the third frequency of occurrence;
将所述第一出现频次与所述第三出现频次之间的商确定为所述目标识别图像分块的识别结果;Determine the quotient between the first frequency of occurrence and the third frequency of occurrence as the recognition result of the target recognition image block;
其中,所述S个获取时刻包括所述当前获取时刻和所述当前获取时刻之前的S-1个获取时刻;所述R个获取时刻包括所述当前获取时刻和所述当前获取时刻之前的R-1个获取时刻,R<S。Wherein, the S acquisition moments include the current acquisition moment and S-1 acquisition moments before the current acquisition moment; the R acquisition moments include the current acquisition moment and R acquisition moments before the current acquisition moment. -1 acquisition time, R<S.
在一些实施例中,所述在根据识别结果深度识别出满足标记临界条件时进行图像分块标记,包括:In some embodiments, the image block marking is performed when the critical conditions for marking are met according to the depth of the recognition result, including:
获取预设标记参考值;Get the preset mark reference value;
当所述目标识别图像分块的识别结果对应的商大于或等于所述预设标记参考值时,确定满足标记临界条件,对对应的目标识别图像分块进行对应的标记。When the quotient corresponding to the recognition result of the target recognition image block is greater than or equal to the preset marking reference value, it is determined that the marking critical condition is met, and the corresponding target recognition image block is marked accordingly.
在一些实施例中,所述获取预设标记参考值,包括:In some embodiments, obtaining a preset mark reference value includes:
获取所述目标识别图像分块在当前获取时刻及之前时刻获取得到三维医学影像数据中的出现频次;Obtain the frequency of occurrence of the target recognition image segment in the three-dimensional medical imaging data obtained at the current acquisition time and the previous time;
根据所述出现频次确定所述目标识别图像分块对应的识别类型;Determine the recognition type corresponding to the target recognition image block according to the frequency of occurrence;
获取所述识别类型对应的预设标记参考值;所述识别图像分块集合中的识别图像分块通过每个识别图像分块的出现频次被分类成不少于一个识别类型,一个识别类型对应一个预设标记参考值。Obtain the preset mark reference value corresponding to the recognition type; the recognition image blocks in the recognition image block set are classified into no less than one recognition type based on the frequency of occurrence of each recognition image segment, and one recognition type corresponds to A preset marker reference value.
第二方面,本申请提供一种病变图像分割装置,包括:In a second aspect, this application provides a lesion image segmentation device, including:
图像获取模块,用于获取待分割医学影像图像,对所述待分割医学影像图像进行图像分块操作,得到所述待分割医学影像图像对应的待识别图像分块集合,其中,所述待分割医学影像图像通过体绘制得到;An image acquisition module, configured to acquire a medical image image to be segmented, perform an image segmentation operation on the medical image image to be segmented, and obtain a set of image segments to be identified corresponding to the medical image image to be segmented, wherein the medical image image to be segmented is Medical imaging images are obtained through volume rendering;
指标确定模块,用于确定所述待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过所述各个待识别医学图像分块的病理辅助促进指标从所述待识别图像分块集合中确定得到参照图像分块集合;所述参照图像分块集合中的医学图像分块是所述待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块;An index determination module, configured to determine the pathological auxiliary promotion index of each to-be-identified medical image block in the to-be-identified image block set, and use the pathological auxiliary promotion index of each of the to-be-identified medical image blocks to obtain the pathological auxiliary promotion index from the to-be-identified medical image block. A reference image block set is determined from the identified image block set; the medical image blocks in the reference image block set are to-be-identified medical images whose corresponding pathological auxiliary promotion indicators in the to-be-identified image block set satisfy the identification conditions. image tiles;
分块确定模块,用于根据所述参照图像分块集合确定识别图像分块集合,所述识别图像分块集合中的识别图像分块为可疑图像分块;A block determination module, configured to determine a set of identified image blocks based on the set of reference image blocks, and the identified image blocks in the set of identified image blocks are suspicious image blocks;
分块标记模块,用于根据所述识别图像分块集合进行识别图像分块识别,以及在根据识别结果深度识别出满足标记临界条件时进行图像分块标记;A block marking module, configured to perform recognition image block recognition based on the recognition image block set, and perform image block marking when it is deeply recognized based on the recognition result that the critical condition for marking is met;
图像分割模块,用于通过每个所述待识别医学图像分块的图像分块标记结果对所述待分割医学影像图像进行分割。An image segmentation module, configured to segment the medical image image to be segmented based on the image segment labeling result of each medical image segment to be identified.
第三方面,本申请还提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现以上所述方法中的步骤。In a third aspect, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are implemented.
本申请至少具有的有益效果包括:The at least beneficial effects of this application include:
本申请对待分割医学影像图像进行图像分块得到待分割医学影像图像对应待识别图像分块集合,根据待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块确定参照图像分块集合,根据参照图像分块集合中的可疑图像分块确定识别图像分块集合,并对识别图像分块集合中的识别图像分块进行识别图像分块识别,在根据识别结果深度识别出满足标记临界条件时进行图像分块标记。这样一来,能够自动识别出待分割医学影像图像对应的识别图像分块集合,以对识别图像分块集合进行识别图像分块识别,增加了图像分割的效率。同时,识别图像分块集合中的识别图像分块为可疑图像分块,可疑图像分块可以用于表征可能出现的新增可疑图像分块,因此对可疑图像分块进行识别,以及在根据可疑图像分块的识别结果深度识别出满足标记临界条件时进行图像分块标记,可以对可能出现的新增可疑图像分块进行预先标记,精确快速地完成病变图像的分割。This application performs image segmentation on the medical image image to be segmented to obtain a set of image segments to be identified corresponding to the medical image image to be segmented, and determines the segments of the medical image to be recognized that meet the recognition conditions based on the corresponding pathological auxiliary promotion indicators in the image segment set to be recognized. Referring to the image block set, determine the identification image block set based on the suspicious image blocks in the reference image block set, and perform identification image block recognition on the identification image blocks in the identification image block set, and based on the depth of the recognition result When it is recognized that the critical conditions for marking are met, the image is marked into blocks. In this way, the recognition image block set corresponding to the medical image image to be segmented can be automatically identified to perform recognition image block recognition on the recognition image block set, which increases the efficiency of image segmentation. At the same time, the identified image blocks in the identified image block set are suspicious image blocks. The suspicious image blocks can be used to characterize new suspicious image blocks that may appear. Therefore, the suspicious image blocks are identified and the suspicious image blocks are identified based on the suspicious image blocks. The recognition results of image blocks are deeply recognized and marked when the critical conditions for marking are met. New suspicious image blocks that may appear can be pre-marked to accurately and quickly complete the segmentation of lesion images.
应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,而非限制本申请的技术方案。It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but do not limit the technical solution of the present application.
附图说明Description of the drawings
此处的附图被并入说明书中并构成本说明书的一部分,这些附图示出了符合本申请的实施例,并与说明书一起用于说明本申请的技术方案。The accompanying drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application, and together with the description, are used to explain the technical solutions of the present application.
图1为本申请实施例提供的一种基于人工智能的病变图像分割方法的实现流程示意图。Figure 1 is a schematic flow chart of the implementation of an artificial intelligence-based lesion image segmentation method provided by an embodiment of the present application.
图2为本申请实施例提供的一种病变图像分割装置的组成结构示意图。Figure 2 is a schematic structural diagram of a lesion image segmentation device provided by an embodiment of the present application.
图3为本申请实施例提供的一种计算机设备的硬件实体示意图。Figure 3 is a schematic diagram of a hardware entity of a computer device provided by an embodiment of the present application.
具体实施方式Detailed ways
为了使本申请的目的、技术方案和优点更加清楚,下面结合附图和实施例对本申请的技术方案进一步详细阐述,所描述的实施例不应视为对本申请的限制,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例,都属于本申请保护的范围。In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application are further elaborated below in conjunction with the accompanying drawings and examples. The described embodiments should not be regarded as limiting the present application. Those of ordinary skill in the art will All other embodiments obtained without creative work fall within the scope of protection of this application.
在以下的描述中,涉及到“一些实施例”,其描述了所有可能实施例的子集,但是可以理解,“一些实施例”可以是所有可能实施例的相同子集或不同子集,并且可以在不冲突的情况下相互结合。所涉及的术语“第一/第二/第三”仅仅是区别类似的对象,不代表针对对象的特定排序,可以理解地,“第一/第二/第三”在允许的情况下可以互换特定的顺序或先后次序,以使这里描述的本申请实施例能够以除了在这里图示或描述的以外的顺序实施。In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict. The terms "first/second/third" involved are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first/second/third" can be used interchangeably if permitted. The specific order or sequence may be changed so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein.
除非另有定义,本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中所使用的术语只是为了描述本申请的目的,不是旨在限制本申请。Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terminology used herein is for the purpose of describing the application only and is not intended to be limiting.
本申请实施例提供一种基于人工智能的病变图像分割方法,该方法可以由计算机设备的处理器执行。其中,计算机设备可以指的是服务器、笔记本电脑、平板电脑、台式计算机、智能电视、移动设备(例如移动电话、便携式视频播放器、个人数字助理)等具备数据处理能力的设备。The embodiment of the present application provides an artificial intelligence-based lesion image segmentation method, which can be executed by a processor of a computer device. Among them, computer equipment may refer to servers, laptops, tablets, desktop computers, smart TVs, mobile devices (such as mobile phones, portable video players, personal digital assistants) and other devices with data processing capabilities.
图1为本申请实施例提供的一种基于人工智能的病变图像分割方法的实现流程示意图,如图1所示,该方法包括如下操作101至操作105:Figure 1 is a schematic flow chart of the implementation of an artificial intelligence-based lesion image segmentation method provided by an embodiment of the present application. As shown in Figure 1, the method includes the following operations 101 to 105:
操作S101,获取待分割医学影像图像,对待分割医学影像图像进行图像分块操作,得到待分割医学影像图像对应的待识别图像分块集合。In operation S101, a medical image image to be segmented is acquired, and an image block operation is performed on the medical image image to be segmented to obtain a set of image blocks to be identified corresponding to the medical image image to be segmented.
本申请实施例中,待分割医学影像图像通过体绘制(Volume Rendering)得到,待分割医学影像图像是目标三维医学影像数据中的任意一个切片,目标三维医学影像数据是在当前获取时刻获取得到,目标三维医学影像数据中包括不少于一个待分割医学影像图像。获取到待分割医学影像图像之后,可以对待分割医学影像图像进行图像分块操作,得到待分割医学影像图像对应的待识别图像分块集合,待识别图像分块集合包括图像分块操作得到的不少于一个待识别医学图像分块。本申请实施例中,图像分块操作的过程具体可以是对该待分割医学影像图像进行基础图像分割的过程,例如按照像素灰度与预设灰度阈值进行分割,或者通过边缘检测(如Canny边缘检测、Sobel算子)的方式将图像分割为不同的图像分块,在其他实施方式中,还可以按照均值分割的方式,将待分割医学影像图像切分为像素尺寸相同的多个图像分块。In the embodiment of this application, the medical image image to be segmented is obtained through volume rendering (Volume Rendering). The medical image image to be segmented is any slice in the target three-dimensional medical image data. The target three-dimensional medical image data is obtained at the current acquisition time. The target three-dimensional medical image data includes no less than one medical image image to be segmented. After obtaining the medical image image to be segmented, the image segmentation operation can be performed on the medical image image to be segmented to obtain the image segmentation set to be identified corresponding to the medical image image to be segmented. The image segmentation set to be recognized includes the different image segments obtained by the image segmentation operation. Less than one medical image patch to be identified. In the embodiment of the present application, the process of image segmentation operation may specifically be a process of basic image segmentation of the medical imaging image to be segmented, such as segmentation according to pixel grayscale and preset grayscale threshold, or through edge detection (such as Canny Edge detection, Sobel operator) method is used to segment the image into different image segments. In other embodiments, the medical image image to be segmented can also be segmented into multiple image segments with the same pixel size according to the mean segmentation method. piece.
操作S102,确定待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过各个待识别医学图像分块的病理辅助促进指标从待识别图像分块集合中确定得到参照图像分块集合。Operation S102, determine the pathological auxiliary promotion index of each medical image block to be identified in the set of image blocks to be identified, and determine the reference from the set of image blocks to be identified through the pathological auxiliary promotion index of each medical image block to be identified. A collection of image tiles.
本申请实施例中,待识别医学图像分块的病理辅助促进指标是用于进行二次图像分割的评估指标,评估指标的参考依据为对病理的识别促进元素,具体可以至少包括以下指标中的一个:待识别医学图像分块的内关联性评价指标、待识别医学图像分块的外关联性评价指标;参照图像分块集合中的医学图像分块是待识别图像分块集合中对应病理辅助促进指标满足识别条件的待识别医学图像分块,即将待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块确定为参照图像分块集合中的医学图像分块。例如,针对待识别图像分块集合中分块尺寸大于尺寸阈值的任意一个第一待识别医学图像分块,尺寸阈值例如为0.5mm×0.5mm,具体和图像模态(如CT、MRI)、具体的疾病类型和病灶特征相关,具体不做限定。确定第一待识别医学图像分块的病理辅助促进指标包括:确定第一待识别医学图像分块的内关联性评价指标和第一待识别医学图像分块的外关联性评价指标,以及基于第一待识别医学图像分块的内关联性评价指标和第一待识别医学图像分块的外关联性评价指标确定第一待识别医学图像分块的病理辅助促进指标。此时,第一待识别医学图像分块的病理辅助促进指标满足识别条件,例如是第一待识别医学图像分块的病理辅助促进指标大于或等于第一指标值。其他实施方式中,针对待识别图像分块集合中分块尺寸小于或等于尺寸阈值的任意一个第二待识别医学图像分块,确定第二待识别医学图像分块的病理辅助促进指标包括:确定第二待识别医学图像分块的外关联性评价指标,将第二待识别医学图像分块的外关联性评价指标作为第二待识别医学图像分块的病理辅助促进指标,第二待识别医学图像分块的病理辅助促进指标满足识别条件是指第二待识别医学图像分块的病理辅助促进指标大于或等于第二指标值。In the embodiment of the present application, the pathological auxiliary promotion index of the medical image segmentation to be identified is an evaluation index used for secondary image segmentation. The reference basis of the evaluation index is the recognition promotion element of pathology, which may include at least one of the following indicators. One: the internal correlation evaluation index of the medical image block to be identified, the external correlation evaluation index of the medical image block to be identified; the medical image block in the reference image block set is the corresponding pathological auxiliary in the image block set to be identified To-be-identified medical image blocks whose promotion indicators meet the recognition conditions are determined as medical image blocks in the reference image block set whose corresponding pathological auxiliary promotion indicators satisfy the recognition conditions in the set of image blocks to be identified. . For example, for any first medical image block to be identified in the set of image blocks to be identified whose block size is greater than the size threshold, the size threshold is, for example, 0.5 mm × 0.5 mm, specifically depending on the image modality (such as CT, MRI), The specific disease type is related to the lesion characteristics and is not specifically limited. Determining the pathological auxiliary promotion index of the first medical image block to be identified includes: determining the internal correlation evaluation index of the first medical image block to be identified and the external correlation evaluation index of the first medical image block to be identified, and based on the first medical image block to be identified. The internal correlation evaluation index of the first medical image block to be identified and the external correlation evaluation index of the first medical image block to be identified determine the pathological auxiliary promotion index of the first medical image block to be identified. At this time, the pathology auxiliary promotion index of the first medical image block to be identified satisfies the identification condition, for example, the pathology auxiliary promotion index of the first medical image block to be identified is greater than or equal to the first index value. In other embodiments, for any second medical image block to be identified whose block size is less than or equal to the size threshold in the set of image blocks to be identified, determining the pathological auxiliary promotion index of the second medical image block to be identified includes: determining The external correlation evaluation index of the second medical image block to be identified is used as the pathological auxiliary promotion index of the second medical image block to be identified. The second medical image block to be identified is The pathological auxiliary promotion index of the image block satisfying the identification condition means that the pathology auxiliary promotion index of the second medical image block to be identified is greater than or equal to the second index value.
可选地,待识别图像分块集合中包括不少于一个第一待识别医学图像分块,可以依据各个第一待识别医学图像分块的病理辅助促进指标递减的次序对各个第一待识别医学图像分块进行排列,获得第一待识别医学图像分块序列,将第一待识别医学图像分块序列中位于第一分布次序前的第一待识别医学图像分块确定为参照图像分块集合中的医学图像分块,其中,第一分布次序为第一待识别医学图像分块序列的前M个。按照相同思路,待识别图像分块集合中包括不少于一个第二待识别医学图像分块,也依据各个第二待识别医学图像分块的病理辅助促进指标递减的次序对各个第二待识别医学图像分块进行排列,得到第二待识别医学图像分块序列,将第二待识别医学图像分块序列中位于第二分布次序前的第二待识别医学图像分块确定为参照图像分块集合中的医学图像分块;第二分布次序是指第二待识别医学图像分块序列的前N个,M和N的具体数值可以根据实际需要进行设置。Optionally, the set of image blocks to be identified includes no less than one first medical image block to be identified, and each first to be identified medical image block can be classified according to the order of decreasing pathological auxiliary promotion index of each first medical image block to be identified. The medical image blocks are arranged to obtain a first medical image block sequence to be identified, and the first medical image block to be identified in the first medical image block sequence to be identified and located before the first distribution order is determined as the reference image block Medical image blocks in the set, where the first distribution order is the first M of the first sequence of medical image blocks to be identified. According to the same idea, the set of image blocks to be identified includes no less than one second medical image block to be identified, and each second medical image block to be identified is also classified according to the order of decreasing pathological auxiliary promotion index of each second medical image block to be identified. The medical image blocks are arranged to obtain a second medical image block sequence to be identified, and the second medical image block to be identified that is located before the second distribution order in the second medical image block sequence to be identified is determined as the reference image block The medical image blocks in the collection; the second distribution order refers to the first N blocks of the second medical image block sequence to be identified. The specific values of M and N can be set according to actual needs.
本申请实施例中,待识别医学图像分块的内关联性评价指标可以通过待识别医学图像分块中的各个切割像素团之间的牵涉性(也即各个切割像素团之间的相关性)来评估待识别医学图像分块是否适于独立存在的程度,每个切割像素团由待识别医学图像分块中的一个像素或多个环绕像素(也就是多个彼此相邻的像素)组成。其中,独立存在的意思是将待识别医学图像分块视为一个独立的区域,待识别医学图像分块的内关联性评价指标越大,则待识别医学图像分块中的各个切割像素团之间的相关性越强,待识别医学图像分块越适于独立存在。待识别医学图像分块的外关联性评价指标可以通过待识别医学图像分块与邻域像素组合得到的待识别融合医学图像分块的不确定性度量值(可以采用概率分布的不确定性来衡量,例如通过统计待识别融合医学图像分块中每个像素值的频次,得到一个像素值的直方图,然后将每个像素值的频次除以待识别融合医学图像分块的总像素数,得到每个像素值的概率,接着对每个概率值进行对数运算,并乘以对数的负数,最后对所有像素值的结果求和,得到待识别融合医学图像分块的不确定性度量值)来评估待识别医学图像分块适于独立存在的程度。邻域像素可以包括待识别医学图像分块在目标三维医学影像数据中的环绕像素;待识别融合医学图像分块的不确定性度量值用于表征待识别融合医学图像分块的不可预测性;待识别融合医学图像分块的不确定性度量值越小,说明待识别融合医学图像分块的不可预测性越小,邻域像素与待识别医学图像分块组成部位的可能性越高。也就是说,待识别融合医学图像分块的不确定性度量值越小,待识别医学图像分块的外关联性评价指标就越小,待识别医学图像分块越不适于独立存在。参照图像分块集合中的医学图像分块是基于待识别图像分块集合中适于独立存在的待识别医学图像分块确定得到的。针对待识别图像分块集合中分块尺寸不小于尺寸阈值的任意一个第一待识别医学图像分块,确定第一待识别医学图像分块的病理辅助促进指标包括以下操作:In the embodiment of the present application, the internal correlation evaluation index of the medical image block to be identified can be based on the involvement between each cut pixel group in the medical image block to be identified (that is, the correlation between each cut pixel group) To evaluate whether the medical image block to be identified is suitable for independent existence, each cutting pixel cluster consists of one pixel or multiple surrounding pixels (that is, multiple adjacent pixels) in the medical image block to be identified. Among them, independent existence means that the medical image block to be identified is regarded as an independent area. The greater the internal correlation evaluation index of the medical image block to be identified, the smaller the number of cut pixel clusters in the medical image block to be identified. The stronger the correlation between them, the more suitable the medical image blocks to be identified are to exist independently. The external correlation evaluation index of the medical image block to be identified can be obtained by combining the medical image block to be identified and the neighborhood pixels to obtain the uncertainty measurement value of the fused medical image block to be identified (the uncertainty of the probability distribution can be used to determine Measurement, for example, by counting the frequency of each pixel value in the fused medical image block to be identified, a histogram of pixel values is obtained, and then dividing the frequency of each pixel value by the total number of pixels in the fused medical image block to be identified, Obtain the probability of each pixel value, then perform a logarithmic operation on each probability value and multiply it by the negative number of the logarithm. Finally, sum the results of all pixel values to obtain the uncertainty measure of the fused medical image block to be identified. value) to evaluate the extent to which the medical image blocks to be identified are suitable for independent existence. The neighborhood pixels may include surrounding pixels of the medical image block to be identified in the target three-dimensional medical imaging data; the uncertainty measurement value of the fused medical image block to be identified is used to characterize the unpredictability of the fused medical image block to be identified; The smaller the uncertainty measure value of the fused medical image block to be identified, the smaller the unpredictability of the fused medical image block to be identified, and the higher the possibility that the neighborhood pixels are components of the medical image block to be identified. That is to say, the smaller the uncertainty measurement value of the fused medical image block to be identified, the smaller the external correlation evaluation index of the medical image block to be identified, and the less suitable the medical image block to be identified to exist independently. The medical image blocks in the reference image block set are determined based on the medical image blocks to be identified that are suitable for independent existence in the image block set to be identified. For any first medical image block to be identified whose block size is not less than the size threshold in the set of image blocks to be identified, determining the pathological auxiliary promotion index of the first medical image block to be identified includes the following operations:
操作一、确定第一待识别医学图像分块的内关联性评价指标。Operation 1: Determine the internal correlation evaluation index of the first medical image block to be identified.
具体地,可以确定第一待识别医学图像分块对应的像素团分组,任意一个像素团分组通过对第一待识别医学图像分块进行切割得到的多个切割像素团构成,每个切割像素团由第一待识别医学图像分块中的一个像素或多个环绕像素组成。针对任意一个像素团分组,获取像素团分组中的每个切割像素团在目标三维医学影像数据中出现的置信度,通过每个切割像素团在目标三维医学影像数据中出现的置信度和第一待识别医学图像分块在目标三维医学影像数据中出现的置信度,确定像素团分组所对应的内关联性评价指标。将各个像素团分组对应的内关联性评价指标中的最小内关联性评价指标作为第一待识别医学图像分块的内关联性评价指标。切割像素团在目标三维医学影像数据中出现的置信度为切割像素团在目标三维医学影像数据中的出现次数和目标三维医学影像数据中的图像分块总数之间的商,或者切割像素团在目标三维医学影像数据中的出现次数与目标三维医学影像数据中的图像分块总数之间的商。第一待识别医学图像分块在目标三维医学影像数据中出现的置信度为第一待识别医学图像分块在目标三维医学影像数据中次数与目标三维医学影像数据中的图像分块总数之间的商,或第一待识别医学图像分块在目标三维医学影像数据中次数与目标三维医学影像数据中的图像分块总数的商。Specifically, the pixel cluster group corresponding to the first medical image segment to be identified can be determined. Any pixel cluster group is composed of multiple cut pixel clusters obtained by cutting the first medical image segment to be identified. Each cut pixel cluster is It consists of one pixel or multiple surrounding pixels in the first medical image block to be identified. For any pixel group group, obtain the confidence that each cut pixel group in the pixel group appears in the target 3D medical imaging data, and use the confidence level and first appearance of each cut pixel group in the target 3D medical imaging data The confidence that the medical image segment to be identified appears in the target three-dimensional medical imaging data determines the internal correlation evaluation index corresponding to the pixel cluster grouping. The minimum internal correlation evaluation index among the internal correlation evaluation indicators corresponding to each pixel group group is used as the internal correlation evaluation index of the first medical image block to be identified. The confidence that the cut pixel cluster appears in the target 3D medical imaging data is the quotient between the number of occurrences of the cut pixel cluster in the target 3D medical imaging data and the total number of image blocks in the target 3D medical imaging data, or the number of times the cut pixel cluster appears in the target 3D medical imaging data. The quotient between the number of occurrences in the target three-dimensional medical imaging data and the total number of image blocks in the target three-dimensional medical imaging data. The confidence level that the first medical image segment to be identified appears in the target 3D medical image data is between the number of times the first medical image segment to be identified appears in the target 3D medical image data and the total number of image segments in the target 3D medical image data. , or the quotient of the number of times the first medical image block to be identified is in the target three-dimensional medical image data and the total number of image blocks in the target three-dimensional medical image data.
像素团分组的内关联性评价指标的计算方式可以参照通用的PMI(PointwiseMutual Information)计算公式,此处不做赘述,将多个像素团分组对应的内关联性评价指标中的最小内关联性评价指标作为第一待识别医学图像分块的内关联性评价指标。The calculation method of the internal correlation evaluation index of the pixel cluster group can refer to the general PMI (PointwiseMutual Information) calculation formula. I will not go into details here. The minimum internal correlation evaluation among the internal correlation evaluation indicators corresponding to multiple pixel clusters is used. The indicator is used as the internal correlation evaluation indicator of the first medical image block to be identified.
操作二、确定第一待识别医学图像分块的外关联性评价指标。Operation 2: Determine the external correlation evaluation index of the first medical image block to be identified.
具体地,可以在目标三维医学影像数据中确定第一待识别医学图像分块的邻域像素集合,邻域像素集合中包括不少于一个邻域像素,邻域像素集合中的邻域像素为第一待识别医学图像分块在目标三维医学影像数据中的环绕像素,获取每个邻域像素与第一待识别医学图像分块组合得到的待识别融合医学图像分块分别在目标三维医学影像数据中出现的置信度,以及基于各个待识别融合医学图像分块在目标三维医学影像数据中出现的置信度,确定第一待识别医学图像分块的外关联性评价指标。其中,待识别融合医学图像分块为通过一个邻域像素与第一待识别医学图像分块组合得到。Specifically, the neighborhood pixel set of the first to-be-identified medical image block can be determined in the target three-dimensional medical image data. The neighborhood pixel set includes no less than one neighborhood pixel, and the neighborhood pixels in the neighborhood pixel set are The surrounding pixels of the first medical image block to be identified in the target three-dimensional medical image data are obtained, and the fused medical image blocks to be identified obtained by combining each neighborhood pixel with the first medical image block to be identified are respectively in the target three-dimensional medical image. The external correlation evaluation index of the first medical image segment to be identified is determined based on the confidence level appearing in the data and the appearance confidence level of each fused medical image segment to be identified in the target three-dimensional medical image data. Wherein, the fused medical image block to be identified is obtained by combining a neighborhood pixel and the first medical image block to be identified.
例如,邻域像素集合包括的邻域像素为第一待识别医学图像分块在目标三维医学影像数据中的环绕像素,获取邻域像素集合中的每个环绕像素与第一待识别医学图像分块组合得到的第一待识别融合医学图像分块分别在目标三维医学影像数据中出现的置信度,以及基于各个第一待识别融合医学图像分块在目标三维医学影像数据中出现的置信度获取多个第一不确定性度量值,将多个第一不确定性度量值中的最小不确定性度量值确定为第一待识别医学图像分块的外关联性评价指标。第一待识别融合医学图像分块在目标三维医学影像数据中出现的置信度为第一待识别融合医学图像分块在目标三维医学影像数据中的出现次数与目标三维医学影像数据的图像分块总数之间的商或第一待识别融合医学图像分块在目标三维医学影像数据中的次数与目标三维医学影像数据的图像分块总数的商。For example, the neighborhood pixels included in the neighborhood pixel set are the surrounding pixels of the first medical image segment to be identified in the target three-dimensional medical image data, and each surrounding pixel in the neighborhood pixel set is obtained with the first medical image segment to be identified. The confidence of each first fusion medical image block to be identified appearing in the target three-dimensional medical imaging data obtained by block combination, and the confidence of the appearance of each first fused medical image block to be identified in the target three-dimensional medical imaging data is obtained. A plurality of first uncertainty measurement values, and a minimum uncertainty measurement value among the plurality of first uncertainty measurement values is determined as an external correlation evaluation index of the first medical image segment to be identified. The confidence that the first fused medical image segment to be identified appears in the target 3D medical imaging data is the number of occurrences of the first fused medical image segment to be identified in the target 3D medical imaging data and the image segmentation of the target 3D medical imaging data. The quotient between the total number or the quotient of the number of times the first fused medical image block to be identified is in the target three-dimensional medical image data and the total number of image blocks of the target three-dimensional medical image data.
第一不确定性度量值的计算方式可以参照通用信息熵计算公式,此处不做赘述,作为另一实施方式,邻域像素集合包括的邻域像素为第一待识别医学图像分块在目标三维医学影像数据中的环绕像素,获取邻域像素集合中的每个环绕像素与第一待识别医学图像分块组合得到的第一待识别融合医学图像分块分别在目标三维医学影像数据中出现的置信度,以及基于各个第一待识别融合医学图像分块在目标三维医学影像数据中出现的置信度获取第一不确定性度量值,将第一不确定性度量值作为第一待识别医学图像分块的外关联性评价指标。The calculation method of the first uncertainty measure value can refer to the general information entropy calculation formula, which will not be described in detail here. As another implementation manner, the neighborhood pixels included in the neighborhood pixel set are the first to-be-identified medical image blocks in the target Surrounding pixels in the three-dimensional medical image data are obtained. Each surrounding pixel in the neighborhood pixel set is combined with the first to-be-identified medical image block to obtain the first to-be-identified fused medical image block, which appears in the target three-dimensional medical image data respectively. The confidence level of each first fusion medical image segment to be identified appears in the target three-dimensional medical image data, and the first uncertainty measurement value is obtained based on the confidence level of each first fusion medical image segment to be identified, and the first uncertainty measurement value is used as the first medical image segment to be identified. External correlation evaluation index of image block.
操作三、根据第一待识别医学图像分块的内关联性评价指标和外关联性评价指标确定第一待识别医学图像分块的病理辅助促进指标。Operation 3: Determine the pathological auxiliary promotion index of the first medical image segment to be identified based on the internal correlation evaluation index and the external correlation evaluation index of the first medical image segment to be identified.
具体地,获取第一待识别医学图像分块的内关联性评价指标对应的第一重要性参数,以及获取第一待识别医学图像分块的内关联性评价指标对应的第二重要性参数,根据第一重要性参数、第一待识别医学图像分块的内关联性评价指标、第二重要性参数、第一待识别医学图像分块的外关联性评价指标获取得到所述第一待识别医学图像分块的病理辅助促进指标。第一待识别医学图像分块的病理辅助促进指标的计算公式为:Specifically, the first importance parameter corresponding to the internal correlation evaluation index of the first medical image block to be identified is obtained, and the second importance parameter corresponding to the internal correlation evaluation index of the first medical image block to be identified is obtained, The first to be identified is obtained according to the first importance parameter, the internal correlation evaluation index of the first medical image block to be identified, the second importance parameter, and the external correlation evaluation index of the first medical image block to be identified. Pathology-assisted facilitation metrics for medical image patching. The calculation formula of the pathological auxiliary promotion index of the first medical image block to be identified is:
G=αI+βOG=αI+βO
G为第一待识别医学图像分块的病理辅助促进指标,α为第一重要性参数,I为第一待识别医学图像分块的内关联性评价指标,β为第二重要性参数,O为第一待识别医学图像分块的外关联性评价指标。G is the pathological auxiliary promotion index of the first medical image block to be identified, α is the first importance parameter, I is the internal correlation evaluation index of the first medical image block to be identified, β is the second importance parameter, O is the external correlation evaluation index of the first medical image block to be identified.
针对待识别图像分块集合中分块尺寸不大于尺寸阈值的任意一个第二待识别医学图像分块,确定第二待识别医学图像分块的病理辅助促进指标具体包括:确定第二待识别医学图像分块的外关联性评价指标,将第二待识别医学图像分块的外关联性评价指标作为第二待识别医学图像分块的病理辅助促进指标。确定第二待识别医学图像分块的外关联性评价指标的方式可以参照确定第一待识别医学图像分块的外关联性评价指标。For any second to-be-identified medical image block in the set of to-be-identified image block segments whose block size is not greater than the size threshold, determining the pathological auxiliary promotion index of the second to-be-identified medical image block specifically includes: determining the second to-be-identified medical image block The external correlation evaluation index of the image block is used as the pathological auxiliary promotion index of the second medical image block to be identified. The method of determining the external correlation evaluation index of the second medical image block to be identified may refer to determining the external correlation evaluation index of the first medical image block to be identified.
第二待识别医学图像分块的邻域像素集合包括的邻域像素为第二待识别医学图像分块在目标三维医学影像数据中的环绕像素。可以获取每个环绕像素与第二待识别医学图像分块组合得到的第三待识别融合医学图像分块在目标三维医学影像数据中出现的置信度,以及基于各个第三待识别融合医学图像分块在目标三维医学影像数据中出现的置信度获取第三不确定性度量值;将第三不确定性度量值中的最小不确定性度量值确定为第二待识别医学图像分块的外关联性评价指标。其中,第三待识别融合医学图像分块在目标三维医学影像数据中出现的置信度为第三待识别融合医学图像分块在目标三维医学影像数据中的出现次数与目标三维医学影像数据的图像分块总数之间的商或第三待识别融合医学图像分块在目标三维医学影像数据中的出现次数与目标三维医学影像数据的图像分块总数的商。The neighborhood pixels included in the neighborhood pixel set of the second medical image block to be identified are the surrounding pixels of the second medical image block to be identified in the target three-dimensional medical image data. The confidence of the appearance of the third fusion medical image segment to be identified in the target three-dimensional medical image data obtained by combining each surrounding pixel with the second medical image segment to be identified can be obtained, and based on each third fusion medical image segment to be identified. Obtain the third uncertainty measure value based on the confidence of the block appearing in the target three-dimensional medical image data; determine the minimum uncertainty measure value in the third uncertainty measure value as the external correlation of the second medical image block to be identified sexual evaluation index. Wherein, the confidence of the appearance of the third fused medical image segment to be identified in the target three-dimensional medical imaging data is the number of occurrences of the third fused medical image segment to be identified in the target three-dimensional medical imaging data and the image of the target three-dimensional medical imaging data. The quotient between the total number of blocks or the quotient of the number of occurrences of the third fusion medical image block to be identified in the target three-dimensional medical image data and the total number of image blocks of the target three-dimensional medical imaging data.
其他实施方式中,邻域像素集合包括的邻域像素为第二待识别医学图像分块在目标三维医学影像数据中的环绕像素,可以获取邻域像素集合中的每个环绕像素与第二待识别医学图像分块组合而得的第三待识别融合医学图像分块分别在目标三维医学影像数据中出现的置信度,以及基于各个第三待识别融合医学图像分块在目标三维医学影像数据中出现的置信度获取第三不确定性度量值,将计算得到的第三不确定性度量值作为第二待识别医学图像分块的外关联性评价指标。In other embodiments, the neighborhood pixels included in the neighborhood pixel set are surrounding pixels of the second to-be-identified medical image block in the target three-dimensional medical image data. Each surrounding pixel in the neighborhood pixel set and the second to-be-identified medical image block can be obtained. Identify the confidence that the third fusion medical image blocks to be identified, which are combined by the medical image blocks, appear in the target three-dimensional medical image data, and the appearance of each third fusion medical image block to be identified in the target three-dimensional medical image data. The third uncertainty measure value is obtained from the confidence level that appears, and the calculated third uncertainty measure value is used as the external correlation evaluation index of the second medical image block to be identified.
操作S103,根据参照图像分块集合确定识别图像分块集合。In operation S103, the identified image block set is determined according to the reference image block set.
识别图像分块集合中的识别图像分块是参照图像分块集合中未出现在参考病变图像集中的医学图像分块。待分割医学影像图像为目标三维医学影像数据中的任意一个切片,目标三维医学影像数据是在当前获取时刻获取得到,根据参照图像分块集合确定识别图像分块集合包括:获取参考病变图像集,参考病变图像集为通过正常图像分块以及根据在当前获取时刻之前获取的三维医学影像数据确定的识别图像分块组建的,在参考病变图像集中对参照图像分块集合中的每个医学图像分块进行匹配,基于匹配不成功的医学图像分块确定识别图像分块集合。The recognized image segments in the recognized image segment set are medical image segments in the reference image segment set that do not appear in the reference lesion image set. The medical image image to be segmented is any slice in the target three-dimensional medical image data. The target three-dimensional medical image data is obtained at the current acquisition time. Determining the identified image block set according to the reference image block set includes: obtaining the reference lesion image set, The reference lesion image set is composed of normal image segments and identified image segments determined based on the three-dimensional medical image data acquired before the current acquisition time. In the reference lesion image set, each medical image segment in the reference image segment set is The blocks are matched, and a set of identified image blocks is determined based on the unsuccessfully matched medical image blocks.
可选地,采用识别图像分块集合对参考病变图像集进行迭代,也就是将识别图像分块集合中的识别图像分块添加到参考病变图像集。在参考病变图像集的迭代过程中,对当前的参考病变图像集中的图像分块进行留存,将每次基于待分割医学影像图像确定得到的识别图像分块集合添加到参考病变图像集。基于此,参考病变图像集的完整性得到提高。Optionally, the reference lesion image set is iterated using the recognition image segment set, that is, the recognition image segment in the recognition image segment set is added to the reference lesion image set. During the iterative process of the reference lesion image set, the image blocks in the current reference lesion image set are retained, and the identified image block set determined based on the medical image image to be segmented each time is added to the reference lesion image set. Based on this, the completeness of the reference lesion image set is improved.
操作S104,根据识别图像分块集合进行识别图像分块识别,以及在根据识别结果深度识别出满足标记临界条件时进行图像分块标记。In operation S104, the recognition image block recognition is performed according to the recognition image block set, and the image block marking is performed when it is deeply recognized according to the recognition result that the critical condition for marking is met.
根据识别图像分块集合中的每个识别图像分块在所有三维医学影像数据中的出现频次,对识别图像分块集合中的识别图像分块进行识别,得到每个识别图像分块的识别结果,对每个识别图像分块的识别结果进行识别,在深度识别出满足标记临界条件时进行图像分块标记。其中,所有三维医学影像数据包括在所有获取时刻获取得到全部三维医学影像数据,识别图像分块在所有三维医学影像数据中的出现频次为识别图像分块在所有三维医学影像数据中的出现次数,或者识别图像分块在所有三维医学影像数据中的出现次数与所有三维医学影像数据中的总图像分块数间的商。According to the frequency of occurrence of each recognition image segment in the recognition image segmentation set in all three-dimensional medical imaging data, the recognition image segmentation in the recognition image segmentation set is recognized, and the recognition result of each recognition image segmentation is obtained. , identify the recognition results of each recognized image block, and mark the image block when the critical conditions for marking are met through deep recognition. Among them, all three-dimensional medical imaging data includes all three-dimensional medical imaging data obtained at all acquisition times, and the frequency of occurrence of identified image blocks in all three-dimensional medical imaging data is the number of occurrences of identified image blocks in all three-dimensional medical imaging data, Or identify the quotient between the number of occurrences of image blocks in all three-dimensional medical imaging data and the total number of image blocks in all three-dimensional medical imaging data.
操作S105,通过每个待识别医学图像分块的图像分块标记结果对待分割医学影像图像进行分割。In operation S105, the medical image image to be segmented is segmented based on the image segment labeling result of each medical image segment to be identified.
具体地,将待分割医学影像图像中的各个图像分块按照图像分块标记结果进行标记,得到对应的标记结果,标记的过程即完成待分割医学影像图像的分割。Specifically, each image block in the medical image image to be segmented is marked according to the image block labeling result, and the corresponding labeling result is obtained. The labeling process completes the segmentation of the medical image image to be segmented.
本申请实施例对待分割医学影像图像进行图像分块得到待分割医学影像图像对应待识别图像分块集合,根据待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块确定参照图像分块集合,参照图像分块集合中的医学图像分块是待识别图像分块集合中适于独立存在的待识别医学图像分块,根据参照图像分块集合中的可疑图像分块确定识别图像分块集合,并对识别图像分块集合中的识别图像分块进行识别图像分块识别,在根据识别结果深度识别出满足标记临界条件时进行图像分块标记。这样一来,能够自动识别出待分割医学影像图像对应的识别图像分块集合,以对识别图像分块集合进行识别图像分块识别,增加了图像分割的效率。同时,识别图像分块集合中的识别图像分块为可疑图像分块,可疑图像分块用于表征可能出现的新增可疑图像分块,因此对可疑图像分块进行识别,以及在根据可疑图像分块的识别结果深度识别出满足标记临界条件时进行图像分块标记,可以对可能出现的新增可疑图像分块进行预先标记,精确快速地完成病变图像的分割。In the embodiment of the present application, the medical image image to be segmented is divided into image blocks to obtain a set of image blocks to be identified corresponding to the medical image image to be segmented. The medical image segment to be identified satisfies the identification conditions according to the corresponding pathological auxiliary promotion index in the image block set to be identified. The block determines the reference image block set. The medical image blocks in the reference image block set are medical image blocks to be identified that are suitable for independent existence in the image block set to be identified. According to the suspicious image blocks in the reference image block set, The block determines the recognition image block set, and performs recognition image block recognition on the recognition image block in the recognition image block set, and performs image segmentation marking when it is deeply recognized based on the recognition result that the critical conditions for marking are met. In this way, the recognition image block set corresponding to the medical image image to be segmented can be automatically identified to perform recognition image block recognition on the recognition image block set, thereby increasing the efficiency of image segmentation. At the same time, the identified image blocks in the identified image block set are suspicious image blocks. The suspicious image blocks are used to characterize new suspicious image blocks that may appear. Therefore, the suspicious image blocks are identified and the suspicious image blocks are identified based on the suspicious image blocks. When the block recognition results are deeply recognized and meet the critical conditions for marking, image block labeling can be pre-marked for new suspicious image blocks that may appear, and the segmentation of the lesion image can be completed accurately and quickly.
在另一实施例中,本申请实施例提供的基于人工智能的病变图像分割方法可以包括以下操作:In another embodiment, the artificial intelligence-based lesion image segmentation method provided by the embodiment of the present application may include the following operations:
操作201,获取待分割医学影像图像,对待分割医学影像图像进行图像分块操作,得到待分割医学影像图像对应的待识别图像分块集合。Operation 201: Obtain a medical image image to be segmented, perform an image block operation on the medical image image to be segmented, and obtain a set of image blocks to be identified corresponding to the medical image image to be segmented.
操作202,确定待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过各个待识别医学图像分块的病理辅助促进指标从待识别图像分块集合中确定得到参照图像分块集合。Operation 202: Determine the pathological auxiliary promotion index of each medical image block to be identified in the set of image blocks to be identified, and determine the reference from the set of image blocks to be identified through the pathological auxiliary promotion index of each medical image block to be identified. A collection of image tiles.
操作203,根据参照图像分块集合确定识别图像分块集合。Operation 203: Determine the identified image block set according to the reference image block set.
操作201~操作203的过程可以参考前述操作101~103。For the process of operation 201 to operation 203, please refer to the aforementioned operations 101 to 103.
操作204,根据识别图像分块集合进行识别图像分块识别。Operation 204: Perform recognition image block recognition according to the recognition image block set.
操作204之前,可选地,目标识别图像分块是识别图像分块集合中的任意一个识别图像分块,例如采用图像向量聚类在参考病变图像集中匹配与目标识别图像分块匹配的匹配医学图像分块,如果匹配到,就基于目标识别图像分块和匹配医学图像分块确定新增识别图像分块,比如将目标识别图像分块与匹配医学图像分块进行组合,获得新增识别图像分块。其中,采用图像向量聚类在参考病变图像集中匹配与目标识别图像分块匹配的匹配医学图像分块,具体可以是在参考病变图像集中确定待匹配医学图像分块,通过向量转换算法得到目标识别图像分块的图像特征向量以及待匹配医学图像分块的图像特征向量(获取图像特征向量时,可以是先进行图像预处理,然后进行特征提取,如采用LBP、HOG、颜色直方图或SIFT等方式进行提取,然后进行特征编码,最后对特征向量进行归一化,得到图像特征向量)。当目标识别图像分块的图像特征向量与待匹配医学图像分块的图像特征向量之间的向量差值小于阈值,或者当目标识别图像分块的图像特征向量与待匹配医学图像分块的图像特征向量之间的内积乘积大于预设内积乘积,则确定待匹配医学图像分块是与目标识别图像分块匹配的匹配医学图像分块。Before operation 204, optionally, the target recognition image segment is any recognition image segment in the recognition image segment set, for example, using image vector clustering to match the matching medical image segment matching the target recognition image segment in the reference lesion image set. Image block, if matched, determine the new recognition image block based on the target recognition image block and the matching medical image block, for example, combine the target recognition image block and the matching medical image block to obtain the new recognition image Block. Among them, image vector clustering is used to match the matching medical image blocks that match the target identification image blocks in the reference lesion image set. Specifically, the medical image blocks to be matched can be determined in the reference lesion image set, and the target identification is obtained through a vector conversion algorithm. Image feature vectors of image blocks and image feature vectors of medical image blocks to be matched (when obtaining image feature vectors, you can first perform image preprocessing and then perform feature extraction, such as using LBP, HOG, color histogram or SIFT, etc. method to extract, then perform feature encoding, and finally normalize the feature vector to obtain the image feature vector). When the vector difference between the image feature vector of the target recognition image block and the image feature vector of the medical image block to be matched is less than the threshold, or when the image feature vector of the target recognition image block and the image of the medical image block to be matched If the inner product product between the feature vectors is greater than the preset inner product product, it is determined that the medical image block to be matched is a matching medical image block that matches the target recognition image block.
其他实施方式中,可以通过图像特征向量聚类在参考病变图像集中匹配与目标识别图像分块匹配的匹配医学图像分块,如果匹配到,则根据目标识别图像分块和匹配医学图像分块确定新增识别图像分块,比如将目标识别图像分块与匹配医学图像分块进行组合,获得新增识别图像分块,还可以根据新增识别图像分块对识别图像分块集合进行迭代,以及基于迭代后的识别图像分块集合对参考病变图像集进行迭代,即将迭代后的识别图像分块集合中的识别图像分块添加至参考病变图像集中。基于此,将参考病变图像集中的匹配医学图像分块与目标识别图像分块进行组合,可以获得新增可疑图像分块,对识别图像分块集合进行扩展,增加识别区间。In other embodiments, matching medical image blocks that match the target recognition image blocks can be matched in the reference lesion image set through image feature vector clustering. If matched, the determination is based on the target recognition image blocks and the matching medical image blocks. Add new recognition image blocks, such as combining target recognition image blocks with matching medical image blocks to obtain new recognition image blocks. You can also iterate the recognition image block collection based on the new recognition image blocks, and The reference lesion image set is iterated based on the iterated recognition image segment set, that is, the recognition image segments in the iterated recognition image segment set are added to the reference lesion image set. Based on this, by combining the matching medical image blocks in the reference lesion image set with the target identification image blocks, new suspicious image blocks can be obtained, the identification image block set can be expanded, and the identification interval can be increased.
在操作204中,根据识别图像分块集合进行识别图像分块识别具体可以包括以下策略中的一个或多个:In operation 204, the recognition of image segmentation based on the recognition image segmentation set may specifically include one or more of the following strategies:
策略一、先获取识别图像分块集合中的每个识别图像分块在所有三维医学影像数据中的出现频次,其中,所有三维医学影像数据包括在所有获取时刻获取得到全部三维医学影像数据,识别图像分块在所有三维医学影像数据中的出现频次是识别图像分块在所有三维医学影像数据中的出现次数,或者识别图像分块在所有三维医学影像数据中的出现次数与所有三维医学影像数据中的图像分块总数之间的商。Strategy 1: First obtain the frequency of occurrence of each recognized image block in the recognized image block set in all three-dimensional medical imaging data. Among them, all three-dimensional medical imaging data include all three-dimensional medical imaging data obtained at all acquisition times. Recognition The frequency of occurrence of image blocks in all three-dimensional medical imaging data is to identify the number of occurrences of image blocks in all three-dimensional medical imaging data, or to identify the number of occurrences of image blocks in all three-dimensional medical imaging data and all three-dimensional medical imaging data. The quotient between the total number of image patches in .
策略二、目标识别图像分块是识别图像分块集合中的任意一个识别图像分块,对目标识别图像分块进行识别时,获取目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,以及获取目标识别图像分块在目标获取时刻获取得到三维医学影像数据中的第二出现频次,目标获取时刻为S个获取时刻中除R个获取时刻之后的获取时刻,计算第一出现频次与第二出现频次之间的商;将第一出现频次与第二出现频次之间的商确定为目标识别图像分块的识别结果。对目标识别图像分块进行识别可参考以下公式:Strategy 2. Target recognition image segmentation is to recognize any recognition image segmentation in the image segmentation set. When identifying the target recognition image segmentation, the target recognition image segmentation is obtained at R acquisition moments to obtain three-dimensional medical imaging data. The first frequency of occurrence in , and the second frequency of occurrence in the three-dimensional medical imaging data obtained by acquiring the target recognition image block at the target acquisition time. The target acquisition time is the acquisition time after the R acquisition moments among the S acquisition moments. Calculate the quotient between the first frequency of appearance and the second frequency of appearance; determine the quotient between the first frequency of appearance and the second frequency of appearance as the recognition result of the target recognition image block. To identify target recognition image blocks, you can refer to the following formula:
θ=(i+t)÷(j+t)θ=(i+t)÷(j+t)
θ为目标识别图像分块的识别结果,i为目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,j为目标识别图像分块在目标获取时刻获取得到三维医学影像数据中的第二出现频次,t为常数。S个获取时刻包括当前获取时刻和当前获取时刻之前的S-1个获取时刻;R个获取时刻包括当前获取时刻和当前获取时刻之前的R-1个获取时刻,R<S。目标识别图像分块通过策略二的识别结果可以表征目标识别图像分块在R个获取时刻的第一出现频次相较目标识别图像分块在目标获取时刻内第二出现频次的增加值。θ is the recognition result of the target recognition image block, i is the first frequency of occurrence in the three-dimensional medical imaging data obtained by the target recognition image block at R acquisition times, and j is the three-dimensional target recognition image block obtained at the target acquisition time. The second frequency of occurrence in medical imaging data, t is a constant. The S acquisition moments include the current acquisition time and S-1 acquisition moments before the current acquisition time; the R acquisition moments include the current acquisition time and R-1 acquisition moments before the current acquisition time, R<S. The recognition results of the target recognition image segmentation through strategy 2 can represent the increased value of the first frequency of appearance of the target recognition image segmentation at R acquisition times compared to the second appearance frequency of the target recognition image segmentation within the target acquisition time.
策略三、目标识别图像分块是识别图像分块集合中的任意一个识别图像分块,对目标识别图像分块进行识别包括:获取目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,以及获取目标识别图像分块在S个获取时刻获取得到三维医学影像数据中的第三出现频次,获取第一出现频次与第三出现频次之间的商,将第一出现频次与第三出现频次之间的商确定为目标识别图像分块的识别结果。对目标识别图像分块进行识别的公式为:Strategy 3. Target recognition image segmentation is to recognize any recognition image segment in the image segmentation set. Recognition of the target recognition image segmentation includes: acquiring the target recognition image segmentation and obtaining three-dimensional medical imaging data at R acquisition moments. The first frequency of occurrence in , and the target recognition image segmentation is obtained at S acquisition moments to obtain the third frequency of occurrence in the three-dimensional medical imaging data, the quotient between the first frequency of occurrence and the third frequency of occurrence is obtained, and the first frequency of occurrence is obtained The quotient between the frequency of occurrence and the third frequency of occurrence is determined as the recognition result of the target recognition image block. The formula for identifying target recognition image blocks is:
λ=i÷ε λ=i÷ε
λ为目标识别图像分块通过策略三得到的识别结果,i为目标识别图像分块在R个获取时刻获取得到三维医学影像数据中的第一出现频次,ɛ为目标识别图像分块在S个获取时刻获取得到三维医学影像数据中的第三出现频次。目标识别图像分块通过策略三的识别结果用于表征目标识别图像分块在R个获取时刻内的第一出现频次占目标识别图像分块在S个获取时刻内第三出现频次的比例。 λ is the recognition result obtained by the target recognition image segmentation through strategy three, i is the first frequency of occurrence of the target recognition image segmentation in the three-dimensional medical imaging data obtained at R acquisition moments, ɛ is the target recognition image segmentation at S times The acquisition time is to obtain the third frequency of occurrence in the three-dimensional medical imaging data. The recognition result of the target recognition image segment through strategy three is used to represent the ratio of the first frequency of appearance of the target recognition image segment within the R acquisition moments to the third frequency of appearance of the target recognition image segment within the S acquisition moments.
操作205,获取预设标记参考值。Operation 205: Obtain a preset mark reference value.
识别图像分块集合中的识别图像分块通过每个识别图像分块在所有三维医学影像数据中的出现频次被分类成不少于一个识别类型,一个识别类型即对应一个预设标记参考值。获取预设标记参考值包括:获取目标识别图像分块在当前获取时刻及之前时刻(即所有获取时刻)获取得到三维医学影像数据中的出现频次;根据出现频次确定目标识别图像分块对应的识别类型,从而获取识别类型对应的预设标记参考值。比如,识别图像分块集合中的识别图像分块通过每个识别图像分块在所有三维医学影像数据中的出现频次被分类成多数据识别类型、一般数据识别类型和少数据识别类型,多数据识别类型对应多数据预设标记参考值,一般数据识别类型对应一般数据预设标记参考值,少数据识别类型对应少数据预设标记参考值,当根据目标识别图像分块在所有三维医学影像数据中的出现频次确定目标识别图像分块为多数据类别时,获取到的预设标记参考值为多数据预设标记参考值。The recognition image blocks in the recognition image block set are classified into no less than one recognition type based on the frequency of occurrence of each recognition image segment in all three-dimensional medical imaging data. One recognition type corresponds to a preset mark reference value. Obtaining the preset mark reference value includes: obtaining the frequency of occurrence of the target recognition image block in the current acquisition time and the previous time (i.e., all acquisition times) in the three-dimensional medical imaging data; determining the identification corresponding to the target recognition image block based on the frequency of occurrence Type to obtain the preset mark reference value corresponding to the identification type. For example, the recognition image blocks in the recognition image block set are classified into multiple data recognition types, general data recognition types and few data recognition types based on the frequency of occurrence of each recognition image segment in all three-dimensional medical imaging data. The recognition type corresponds to the multi-data preset mark reference value, the general data recognition type corresponds to the general data preset mark reference value, and the less data recognition type corresponds to the less data preset mark reference value. When the target recognition image is divided into blocks in all three-dimensional medical imaging data The frequency of occurrence in determines that when the target recognition image is divided into multiple data categories, the obtained preset mark reference value is the multi-data preset mark reference value.
操作206,当目标识别图像分块的识别结果对应的商大于或等于预设标记参考值时,确定满足标记临界条件,对对应的目标识别图像分块进行对应的标记。Operation 206: When the quotient corresponding to the recognition result of the target recognition image segment is greater than or equal to the preset marking reference value, it is determined that the marking critical condition is met, and the corresponding target recognition image segment is marked accordingly.
当目标识别图像分块的识别结果对应的商不小于预设标记参考值,则确定满足标记临界条件。可选地,依据各个识别图像分块的识别结果对应的商递减的次序对各个识别图像分块进行排列,得到识别图像分块序列,将识别图像分块序列中位于第三分布次序之前的识别图像分块确定为满足标记临界条件的识别图像分块,第三分布次序例如是识别图像分块序列中的前K个识别图像分块。When the quotient corresponding to the recognition result of the target recognition image block is not less than the preset mark reference value, it is determined that the mark critical condition is met. Optionally, arrange the recognition image blocks in the order of decreasing quotients corresponding to the recognition results of each recognition image block to obtain the recognition image block sequence, and classify the recognition image blocks in the sequence before the third distribution order. The image blocks are determined as recognition image blocks that satisfy the labeling critical condition, and the third distribution order is, for example, the first K recognition image blocks in the recognition image block sequence.
操作207,根据图像分块标记策略进行图像分块标记,完成图像分割。Operation 207: Mark the image blocks according to the image block labeling strategy to complete the image segmentation.
可以获取待分割医学影像图像,对待分割医学影像图像进行图像分块操作,得到待分割医学影像图像对应的待识别图像分块集合;确定待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过各个待识别医学图像分块的病理辅助促进指标从待识别图像分块集合中确定得到参照图像分块集合;参照图像分块集合中的医学图像分块是待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块;根据参照图像分块集合确定识别图像分块集合,所述识别图像分块集合中的识别图像分块为可疑图像分块;根据识别图像分块集合进行识别图像分块识别,以及在根据识别结果识别到满足条件时进行标记。The medical image image to be segmented can be obtained, and the medical image image to be segmented can be subjected to an image segmentation operation to obtain a set of image segments to be identified corresponding to the medical image image to be segmented; and each medical image segment to be recognized in the image segmentation set to be recognized can be determined. The pathological auxiliary promotion index of each medical image block to be identified is determined from the image block set to be identified to obtain the reference image block set; the medical image block in the reference image block set is to be identified The medical image segments to be identified whose corresponding pathological auxiliary promotion indicators in the image segmentation set meet the recognition conditions; the recognition image segmentation set is determined based on the reference image segmentation set, and the recognition image segmentation in the recognition image segmentation set is suspicious Image segmentation; perform recognition of image segmentation based on the set of recognized image segments, and mark when conditions are met based on the recognition results.
本申请实施例对待分割医学影像图像进行图像分块得到待分割医学影像图像对应待识别图像分块集合,根据待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块确定参照图像分块集合,参照图像分块集合中的医学图像分块是待识别图像分块集合中适于独立存在的待识别医学图像分块,根据参照图像分块集合中的可疑图像分块确定识别图像分块集合,并对识别图像分块集合中的识别图像分块进行识别图像分块识别,在根据识别结果深度识别出满足标记临界条件时进行图像分块标记。这样一来,能够自动识别出待分割医学影像图像对应的识别图像分块集合,以对识别图像分块集合进行识别图像分块识别,增加了图像分割的效率。同时,识别图像分块集合中的识别图像分块为可疑图像分块,可疑图像分块可以用于表征可能出现的新增可疑图像分块,因此对可疑图像分块进行识别,以及在根据可疑图像分块的识别结果深度识别出满足标记临界条件时进行图像分块标记,可以对可能出现的新增可疑图像分块进行预先标记,精确快速地完成病变图像的分割。同时,通过将识别图像分块集合中的识别图像分块,与参考病变图像集中与识别图像分块匹配的匹配医学图像分块进行组合,可对可疑图像分块进行还原,将识别图像分块集合中的识别图像分块,与参考病变图像集中与识别图像分块匹配的匹配医学图像分块进行组合,可以组合得到新增可疑图像分块,可对识别图像分块集合进行扩展示,增加识别区间,根据识别图像分块在所有三维医学影像数据中的出现频次对识别图像分块进行分类,以及为每个识别类型匹配一预设标记参考值,有助于匹配不同出现频次的识别图像分块,提高图像分块标记的准确性。In the embodiment of the present application, the medical image image to be segmented is divided into image blocks to obtain a set of image blocks to be identified corresponding to the medical image image to be segmented. The medical image segment to be identified satisfies the identification conditions according to the corresponding pathological auxiliary promotion index in the image block set to be identified. The blocks determine the reference image block set. The medical image blocks in the reference image block set are medical image blocks to be identified that are suitable for independent existence in the image block set to be identified. According to the suspicious image blocks in the reference image block set, The block determines the recognition image block set, and performs recognition image block recognition on the recognition image block in the recognition image block set. When it is deeply recognized based on the recognition result that the critical condition for marking is met, the image segmentation is marked. In this way, the recognition image block set corresponding to the medical image image to be segmented can be automatically identified to perform recognition image block recognition on the recognition image block set, which increases the efficiency of image segmentation. At the same time, the identified image blocks in the identified image block set are suspicious image blocks. The suspicious image blocks can be used to characterize new suspicious image blocks that may appear. Therefore, the suspicious image blocks are identified and the suspicious image blocks are identified based on the suspicious image blocks. The recognition results of image blocks are deeply recognized and marked when the critical conditions for marking are met. New suspicious image blocks that may appear can be pre-marked to accurately and quickly complete the segmentation of lesion images. At the same time, by combining the identified image blocks in the identified image block set with the matching medical image blocks in the reference lesion image set that match the identified image blocks, the suspicious image blocks can be restored and the identified image blocks can be restored. The identified image blocks in the set are combined with the matching medical image blocks in the reference lesion image set that match the identified image blocks. New suspicious image blocks can be obtained by combining the identified image blocks. The set of identified image blocks can be expanded and added. Recognition interval, classify the recognition image blocks according to the frequency of occurrence of the recognition image blocks in all three-dimensional medical imaging data, and match a preset mark reference value for each recognition type, which helps to match the recognition images with different frequency of occurrence Blocking to improve the accuracy of image block labeling.
基于前述的实施例,本申请实施例提供一种病变图像分割装置,该装置所包括的各单元、以及各单元所包括的各模块,可以通过计算机设备中的处理器来实现;当然也可通过具体的逻辑电路实现;在实施的过程中,处理器可以为中央处理器(Central ProcessingUnit,CPU)、微处理器(Microprocessor Unit,MPU)、数字信号处理器(Digital SignalProcessor,DSP)或现场可编程门阵列(Field Programmable Gate Array,FPGA)等。Based on the foregoing embodiments, embodiments of the present application provide a lesion image segmentation device. Each unit included in the device and each module included in each unit can be implemented by a processor in a computer device; of course, it can also be implemented by Specific logic circuit implementation; during the implementation process, the processor can be a central processing unit (CPU), a microprocessor (Microprocessor Unit, MPU), a digital signal processor (Digital SignalProcessor, DSP) or field programmable Gate array (Field Programmable Gate Array, FPGA), etc.
图2为本申请实施例提供的一种病变图像分割装置的组成结构示意图,如图2所示,病变图像分割装置200包括:Figure 2 is a schematic structural diagram of a lesion image segmentation device provided by an embodiment of the present application. As shown in Figure 2, the lesion image segmentation device 200 includes:
图像获取模块210,用于获取待分割医学影像图像,对所述待分割医学影像图像进行图像分块操作,得到所述待分割医学影像图像对应的待识别图像分块集合,其中,所述待分割医学影像图像通过体绘制得到;The image acquisition module 210 is used to acquire a medical image image to be segmented, perform an image segmentation operation on the medical image image to be segmented, and obtain a set of image segments to be identified corresponding to the medical image image to be segmented, wherein the medical image image to be segmented is Segmented medical imaging images are obtained through volume rendering;
指标确定模块220,用于确定所述待识别图像分块集合中的各个待识别医学图像分块的病理辅助促进指标,并通过所述各个待识别医学图像分块的病理辅助促进指标从所述待识别图像分块集合中确定得到参照图像分块集合;所述参照图像分块集合中的医学图像分块是所述待识别图像分块集合中所对应病理辅助促进指标满足识别条件的待识别医学图像分块;The indicator determination module 220 is used to determine the pathological auxiliary promotion index of each medical image block to be identified in the set of image blocks to be identified, and use the pathology auxiliary promotion index of each medical image block to be identified to obtain the pathological auxiliary promotion index from the A reference image block set is determined from the set of image blocks to be identified; the medical image blocks in the reference image block set are to be identified whose corresponding pathological auxiliary promotion indicators in the set of image blocks to be identified satisfy the identification conditions. Medical image segmentation;
分块确定模块230,用于根据所述参照图像分块集合确定识别图像分块集合,所述识别图像分块集合中的识别图像分块为可疑图像分块;The block determination module 230 is configured to determine a set of identified image blocks according to the set of reference image blocks, and the identified image blocks in the set of identified image blocks are suspicious image blocks;
分块标记模块240,用于根据所述识别图像分块集合进行识别图像分块识别,以及在根据识别结果深度识别出满足标记临界条件时进行图像分块标记;The block marking module 240 is configured to perform recognition image block recognition according to the recognition image block set, and perform image block marking when it is deeply recognized according to the recognition result that the critical condition for marking is met;
图像分割模块250,用于通过每个所述待识别医学图像分块的图像分块标记结果对所述待分割医学影像图像进行分割。The image segmentation module 250 is configured to segment the medical image image to be segmented based on the image segment labeling result of each medical image segment to be identified.
以上装置实施例的描述,与上述方法实施例的描述是类似的,具有同方法实施例相似的有益效果。在一些实施例中,本申请实施例提供的装置具有的功能或包含的模块可以用于执行上述方法实施例描述的方法,对于本申请装置实施例中未披露的技术细节,请参照本申请方法实施例的描述而理解。The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. In some embodiments, the functions or modules provided by the device provided by the embodiments of this application can be used to perform the methods described in the above method embodiments. For technical details not disclosed in the device embodiments of this application, please refer to the methods of this application. be understood from the description of the embodiments.
需要说明的是,本申请实施例中,如果以软件功能模块的形式实现上述的基于人工智能的病变图像分割方法,并作为独立的产品销售或使用时,也可以存储在一个计算机可读取存储介质中。基于这样的理解,本申请实施例的技术方案本质上或者说对相关技术做出贡献的部分可以以软件产品的形式体现出来,该软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本申请各个实施例所述方法的全部或部分。而前述的存储介质包括:U盘、移动硬盘、只读存储器(Read Only Memory,ROM)、磁碟或者光盘等各种可以存储程序代码的介质。这样,本申请实施例不限制于任何特定的硬件、软件或固件,或者硬件、软件、固件三者之间的任意结合。It should be noted that in the embodiments of the present application, if the above-mentioned artificial intelligence-based lesion image segmentation method is implemented in the form of a software function module and is sold or used as an independent product, it can also be stored in a computer-readable storage. in the medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of software products in essence or that contribute to related technologies. The software products are stored in a storage medium and include a number of instructions to enable a A computer device (which may be a personal computer, a server, a network device, etc.) executes all or part of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read only memory (Read Only Memory, ROM), magnetic disk or optical disk and other various media that can store program codes. In this way, the embodiments of the present application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.
本申请实施例提供一种计算机设备,包括存储器和处理器,所述存储器存储有可在处理器上运行的计算机程序,所述处理器执行所述程序时实现上述方法中的部分或全部步骤。An embodiment of the present application provides a computer device, including a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor executes the program, some or all of the steps in the above method are implemented.
本申请实施例提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现上述方法中的部分或全部步骤。所述计算机可读存储介质可以是瞬时性的,也可以是非瞬时性的。Embodiments of the present application provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, some or all of the steps in the above method are implemented. The computer-readable storage medium may be transient or non-transitory.
本申请实施例提供一种计算机程序,包括计算机可读代码,在所述计算机可读代码在计算机设备中运行的情况下,所述计算机设备中的处理器执行用于实现上述方法中的部分或全部步骤。Embodiments of the present application provide a computer program, which includes computer readable code. When the computer readable code is run in a computer device, the processor in the computer device executes a part for implementing the above method or All steps.
本申请实施例提供一种计算机程序产品,所述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质,所述计算机程序被计算机读取并执行时,实现上述方法中的部分或全部步骤。该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一些实施例中,所述计算机程序产品具体体现为计算机存储介质,在另一些实施例中,计算机程序产品具体体现为软件产品,例如软件开发包(Software Development Kit,SDK)等等。Embodiments of the present application provide a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, some of the above methods are implemented or All steps. The computer program product can be implemented specifically through hardware, software or a combination thereof. In some embodiments, the computer program product is embodied as a computer storage medium. In other embodiments, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK) and so on.
这里需要指出的是:上文对各个实施例的描述倾向于强调各个实施例之间的不同之处,其相同或相似之处可以互相参考。以上设备、存储介质、计算机程序及计算机程序产品实施例的描述,与上述方法实施例的描述是类似的,具有同方法实施例相似的有益效果。对于本申请设备、存储介质、计算机程序及计算机程序产品实施例中未披露的技术细节,请参照本申请方法实施例的描述而理解。It should be noted here that the above description of various embodiments tends to emphasize the differences between the various embodiments, and the similarities or similarities may be referred to each other. The description of the above embodiments of equipment, storage media, computer programs and computer program products is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the equipment, storage media, computer programs and computer program products of this application, please refer to the description of the method embodiments of this application for understanding.
图3为本申请实施例提供的一种计算机设备的硬件实体示意图,如图3所示,该计算机设备1000的硬件实体包括:处理器1001和存储器1002,其中,存储器1002存储有可在处理器1001上运行的计算机程序,处理器1001执行程序时实现上述任一实施例的方法中的步骤。Figure 3 is a schematic diagram of the hardware entity of a computer device provided by an embodiment of the present application. As shown in Figure 3, the hardware entity of the computer device 1000 includes: a processor 1001 and a memory 1002, where the memory 1002 stores information that can be stored in the processor. The computer program running on 1001 implements the steps in the method of any of the above embodiments when the processor 1001 executes the program.
存储器1002存储有可在处理器上运行的计算机程序,存储器1002配置为存储由处理器1001可执行的指令和应用,还可以缓存待处理器1001以及计算机设备1000中各模块待处理或已经处理的数据(例如,图像数据、音频数据、语音通信数据和视频通信数据),可以通过闪存(FLASH)或随机访问存储器(Random Access Memory,RAM)实现。The memory 1002 stores computer programs that can be run on the processor. The memory 1002 is configured to store instructions and applications executable by the processor 1001. It can also cache data to be processed or processed by the processor 1001 and each module in the computer device 1000. Data (for example, image data, audio data, voice communication data and video communication data) can be realized through flash memory (FLASH) or random access memory (Random Access Memory, RAM).
处理器1001执行程序时实现上述任一项的基于人工智能的病变图像分割方法的步骤。处理器1001通常控制计算机设备1000的总体操作。When the processor 1001 executes the program, the steps of any of the above artificial intelligence-based lesion image segmentation methods are implemented. Processor 1001 generally controls the overall operation of computer device 1000 .
本申请实施例提供一种计算机存储介质,计算机存储介质存储有一个或者多个程序,该一个或者多个程序可被一个或者多个处理器执行,以实现如上任一实施例的基于人工智能的病变图像分割方法的步骤。Embodiments of the present application provide a computer storage medium. The computer storage medium stores one or more programs. The one or more programs can be executed by one or more processors to implement the artificial intelligence-based system as in any of the above embodiments. Steps of the lesion image segmentation method.
这里需要指出的是:以上存储介质和设备实施例的描述,与上述方法实施例的描述是类似的,具有同方法实施例相似的有益效果。对于本申请存储介质和设备实施例中未披露的技术细节,请参照本申请方法实施例的描述而理解。上述处理器可以为目标用途集成电路(Application Specific Integrated Circuit,ASIC)、数字信号处理器(DigitalSignal Processor,DSP)、数字信号处理装置(Digital Signal Processing Device,DSPD)、可编程逻辑装置(Programmable Logic Device,PLD)、现场可编程门阵列(FieldProgrammable Gate Array,FPGA)、中央处理器(Central Processing Unit,CPU)、控制器、微控制器、微处理器中的至少一种。可以理解地,实现上述处理器功能的电子器件还可以为其它,本申请实施例不作具体限定。It should be pointed out here that the above description of the storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding. The above-mentioned processor can be an application specific integrated circuit (ASIC), a digital signal processor (DigitalSignal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), or a programmable logic device (Programmable Logic Device). , PLD), Field Programmable Gate Array (Field Programmable Gate Array, FPGA), Central Processing Unit (Central Processing Unit, CPU), controller, microcontroller, and microprocessor. It is understandable that the electronic device that implements the above processor function can also be other, which is not specifically limited in the embodiments of this application.
上述计算机存储介质/存储器可以是只读存储器(Read Only Memory,ROM)、可编程只读存储器(Programmable Read-Only Memory,PROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory,EPROM)、电可擦除可编程只读存储器(Electrically Erasable Programmable Read-Only Memory,EEPROM)、磁性随机存取存储器(Ferromagnetic Random Access Memory,FRAM)、快闪存储器(Flash Memory)、磁表面存储器、光盘、或只读光盘(Compact Disc Read-Only Memory,CD-ROM)等存储器;也可以是包括上述存储器之一或任意组合的各种终端,如移动电话、计算机、平板设备、个人数字助理等。The above-mentioned computer storage medium/memory can be read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Ferromagnetic Random Access Memory (FRAM), Flash Memory, Magnetic Surface Memory, Memories such as optical disks or Compact Disc Read-Only Memory (CD-ROM); it can also be various terminals including one or any combination of the above memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. .
应理解,说明书通篇中提到的“一个实施例”或“一实施例”意味着与实施例有关的特定特征、结构或特性包括在本申请的至少一个实施例中。因此,在整个说明书各处出现的“在一个实施例中”或“在一实施例中”未必一定指相同的实施例。此外,这些特定的特征、结构或特性可以任意适合的方式结合在一个或多个实施例中。应理解,在本申请的各种实施例中,上述各步骤/过程的序号的大小并不意味着执行顺序的先后,各步骤/过程的执行顺序应以其功能和内在逻辑确定,而不应对本申请实施例的实施过程构成任何限定。上述本申请实施例序号仅仅为了描述,不代表实施例的优劣。需要说明的是,在本文中,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。It will be understood that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that in various embodiments of the present application, the size of the serial numbers of the above steps/processes does not mean the order of execution. The execution order of each step/process should be determined by its function and internal logic, and should not be The implementation process of the embodiments of this application does not constitute any limitations. The above serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes that element.
在本申请所提供的几个实施例中,应该理解到,所揭露的设备和方法,可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,如:多个单元或组件可以结合,或可以集成到另一个系统,或一些特征可以忽略,或不执行。另外,所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口,设备或单元的间接耦合或通信连接,可以是电性的、机械的或其它形式的。In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components may be combined, or can be integrated into another system, or some features can be ignored, or not implemented. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be electrical, mechanical, or other forms. of.
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的,作为单元显示的部件可以是、或也可以不是物理单元;既可以位于一个地方,也可以分布到多个网络单元上;可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。The units described above as separate components may or may not be physically separated; the components shown as units may or may not be physical units; they may be located in one place or distributed to multiple network units; Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外,在本申请各实施例中的各功能单元可以全部集成在一个处理单元中,也可以是各单元分别单独作为一个单元,也可以两个或两个以上单元集成在一个单元中;上述集成的单元既可以采用硬件的形式实现,也可以采用硬件加软件功能单元的形式实现。In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated into one unit; the above-mentioned integration The unit can be implemented in the form of hardware or in the form of hardware plus software functional units.
本领域普通技术人员可以理解:实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成,前述的程序可以存储于计算机可读取存储介质中,该程序在执行时,执行包括上述方法实施例的步骤;而前述的存储介质包括:移动存储设备、只读存储器(Read Only Memory,ROM)、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed through hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the execution includes: The steps of the above method embodiment; and the aforementioned storage media include: mobile storage devices, read-only memory (Read Only Memory, ROM), magnetic disks or optical disks and other various media that can store program codes.
或者,本申请上述集成的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时,也可以存储在一个计算机可读取存储介质中。基于这样的理解,本申请的技术方案本质上或者说对相关技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本申请各个实施例所述方法的全部或部分。而前述的存储介质包括:移动存储设备、ROM、磁碟或者光盘等各种可以存储程序代码的介质。Alternatively, if the integrated units mentioned above in this application are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or that contributes to related technologies. The computer software product is stored in a storage medium and includes a number of instructions to enable a computer. A computer device (which may be a personal computer, a server, a network device, etc.) executes all or part of the methods described in various embodiments of this application. The aforementioned storage media include: mobile storage devices, ROMs, magnetic disks or optical disks and other media that can store program codes.
以上所述,仅为本申请的实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到变化或替换,都应涵盖在本申请的保护范围之内。The above are only embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present application. are covered by the protection scope of this application.
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