WO2021109988A1 - Ct image-based method and device for realizing lung nodule adaptive matching by bone registration - Google Patents
Ct image-based method and device for realizing lung nodule adaptive matching by bone registration Download PDFInfo
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- the invention relates to the technical field of medical equipment, and in particular to a method and device for realizing adaptive matching of lung nodules based on CT image bone registration.
- Lung cancer is one of the most harmful malignant tumors to human health and life.
- Malignant pulmonary nodules are an important manifestation of early lung cancer.
- the growth characteristics of nodules reflect the relationship between the increase in the number or volume of cells in the nodules and time.
- computer-aided detection of lung nodules based on CT images has become a research hotspot in the early diagnosis of lung cancer.
- follow-up observation of CT images can effectively evaluate the growth characteristics of lung nodules over a period of time. , So as to provide a basis for the early detection and accurate diagnosis of lung cancer.
- the existing matching methods mainly include global-based matching methods and local-based matching methods.
- the location and state of lung tissue before and after follow-up are often inconsistent, resulting in large differences between CT images before and after follow-up. Due to the inconsistency of lung CT images and the unpredictability of nodule growth, the matching accuracy of the existing two types of matching methods are relatively low, and matching errors may occur in severe cases.
- embodiments of the present invention provide a method and device for adaptive matching of lung nodules based on CT image skeletal registration.
- an embodiment of the present invention provides a method for adaptive matching of lung nodules based on CT image skeletal registration, which is characterized in that it includes the following steps:
- Three-dimensional point cloud data registration two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated;
- Adaptive matching of lung nodules Based on the registration error, a distance-based method is used to match lung nodules.
- the method for data preparation in the step (1) is:
- the nodule data of the two groups of CT images before and after the preparation follow-up including nodule coordinates, length and short diameter, volume and attributes.
- the method for extracting three-dimensional point cloud data of lungs and bones in the step (2) is:
- the method for extracting the lung area in the step (2.4) is:
- the method for three-dimensional point cloud data registration in the step (3) is:
- Preprocessing of bone 3D point cloud data including: extracting FPFH features of bone 3D point cloud data, and sparsely sampling the bone 3D point cloud data;
- the method for realizing adaptive matching of lung nodules based on CT image skeletal registration further includes the following steps:
- the embodiment of the present invention provides a method and device for adaptive matching of lung nodules based on CT image bone registration.
- One is based on the characteristics of small changes in human bones and is obtained by using bones in CT images before and after follow-up for registration.
- the transformation matrix is used to perform three-dimensional point cloud rigid transformation registration on lung image data and lung nodule data, so as to realize the alignment of lung and lung nodule data before and after follow-up, which can effectively overcome the errors caused by lung deformation;
- the second is to adopt FGR algorithm, this algorithm does not involve iterative sampling, model fitting or local refinement.
- the third is to use RMSE as the lung point cloud registration error , Realizes the adaptive matching of lung nodules, the registration of lung nodules requires less manual intervention, high degree of automation, and accurate registration results; fourth, through the normalization of CT image data, the robustness of the algorithm can be improved It can be widely used in different types of CT equipment and DICOM data with different pixel spacing values.
- FIG. 3 is a schematic diagram of comparison before and after registration of a CT image lung point cloud provided by an embodiment of the present invention
- FIG. 4 is a schematic diagram of a CT image lung nodule matching result provided by an embodiment of the present invention.
- FIG. 5 is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention.
- (1) Data preparation prepare lung CT images and prepare lung nodules data.
- the CT images are two sets of CT images before and after follow-up; the lung nodule data is the lung nodule data reflected by the CT images.
- Three-dimensional point cloud data registration two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated.
- the registration of the two sets of bone 3D point cloud data may use FGR (Fast Global Registration, Fast Global Registration Partially Overlapping 3D Surface Algorithm) or ICP (Iterative Closest Point Iterative Closest Point Algorithm) and other algorithms to improve the conversion matrix. Accuracy, and reduce the amount of calculation.
- Adaptive matching of lung nodules Based on the registration error, a distance-based method is used to match lung nodules.
- the method for data preparation in the step (1) is:
- CT images of the two groups before and after the preparation follow-up adopts the DICOM standard data format, that is, the international standard data format conforming to ISO 12052, and can be sorted in ascending order according to the ImagePosition in the DICOM tag.
- DICOM standard data format that is, the international standard data format conforming to ISO 12052
- nodule data of the two groups of CT images before and after the preparation follow-up including nodule coordinates, length and short diameter, volume and attributes.
- the attributes include but are not limited to calcification, fat, necrosis, density uniformity, CT value, etc.
- the connected area is preliminarily extracted using the binarization method.
- the bone region is extracted to generate bone 3D point cloud data.
- the method for extracting the bone region may be based on the specific CT value range of the bone in the CT image, because the CT value range of the bone is different from other organs and tissues.
- Preprocessing of skeleton 3D point cloud data Extract FPFH (Fast Point Feature Histograms) features of skeleton 3D point cloud data; perform sparse sampling of skeleton 3D point cloud data.
- FPFH Fast Point Feature Histograms
- the FGR algorithm is used for point cloud registration, and the transformation matrix is obtained.
- this step is a schematic diagram of the comparison before and after the registration of the CT image bone point cloud. It can be seen from the figure: the left image is the morphology of the bone point cloud before registration, the images of the two bones before registration are inconsistent and cannot be overlapped; the right image is the morphology of the bone point cloud after registration, the two bones after registration The images are the same, basically completely overlapped.
- this step is a schematic diagram of the comparison before and after the registration of the lung point cloud of the CT image. It can be seen from the figure: the left picture shows the shape of the lung point cloud before registration, the images of the two lungs before registration are inconsistent and cannot be overlapped; the right picture shows the shape of the lung point cloud after registration, after registration The images of the two lungs are the same, almost completely overlapped.
- the method for adaptive matching of lung nodules in the step (4) is:
- the threshold can be obtained by automatically calculating and matching according to the registration error according to a set formula or a set rule; usually, the threshold is positively correlated with the registration error, so that compared to a fixed threshold setting method, it can be used for different registrations. The result is better adaptability.
- the lung nodule searched and determined by traversal if the coordinate of the lung nodule after transformation is less than the threshold, it is determined that the lung nodule is matched successfully; otherwise, it is determined that the lung nodule is matched unsuccessfully.
- FIG 4 it is a schematic diagram of the matching results of a CT image of lung nodules.
- the left picture is the position and coordinates of a nodule in CT before follow-up
- the right picture is the position and coordinates of a nodule in follow-up CT.
- the two images on the left and right are matched nodules determined by the algorithm. It can be seen from the figure that the coordinates of the nodules are different due to the error in the two shots before and after the follow-up, but the position and surrounding tissues of the nodules can be determined.
- the two nodules are the same nodules in different periods, which shows that this method has higher matching accuracy.
- An embodiment of the present invention also provides an electronic device.
- the electronic device may include: a processor 301, a communications interface 302, a memory 303, and a communications bus 304, where: The processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.
- the device embodiments described above are merely illustrative, where the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement it without creative work.
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Abstract
A CT image-based method for realizing lung nodule adaptive matching by bone registration, comprising the following steps: data preparation, extraction of lung and bone point cloud data, three-dimensional point cloud data registration, and lung nodule adaptive matching. In said method, firstly, three-dimensional point cloud rigid transformation registration is performed on lung image data and lung nodule data on the basis of the characteristic of small changes in human bone, so as to achieve the alignment of the lung and the lung nodule data before and after follow-up; secondly, an FGR algorithm is used, and in terms of operation speed and registration accuracy, the FGR algorithm is obviously superior to local refinement algorithms such as ICP; thirdly, the RMSE is used as a lung point cloud registration error, achieving lung nodule adaptive matching, so that the lung nodule registration requires less manual intervention, has a high degree of automation, and achieves an accurate registration result; and fourthly, normalization processing is performed on CT image data, so that the robustness of the algorithm can be improved. The method can be widely applied to CT devices of different models and DICOM data of different pixel spacing values.
Description
本发明涉及医疗设备技术领域,尤其涉及一种基于CT影像骨骼配准实现肺结节自适应匹配的方法与装置。The invention relates to the technical field of medical equipment, and in particular to a method and device for realizing adaptive matching of lung nodules based on CT image bone registration.
肺癌是当今对人类健康与生命危害最大的恶性肿瘤之一。恶性肺结节是早期肺癌的一个重要表现,结节的生长特性反映了结节内细胞数量或体积的增加与时间的关系。随着医疗影像学与计算机技术的飞速发展,基于CT影像的计算机辅助检测肺结节已成为肺癌早期诊断的研究热点,通过CT影像随访观察,可以有效评估肺结节在一段时间内的生长特性,从而为肺癌的早期发现与准确诊断提供依据。Lung cancer is one of the most harmful malignant tumors to human health and life. Malignant pulmonary nodules are an important manifestation of early lung cancer. The growth characteristics of nodules reflect the relationship between the increase in the number or volume of cells in the nodules and time. With the rapid development of medical imaging and computer technology, computer-aided detection of lung nodules based on CT images has become a research hotspot in the early diagnosis of lung cancer. Follow-up observation of CT images can effectively evaluate the growth characteristics of lung nodules over a period of time. , So as to provide a basis for the early detection and accurate diagnosis of lung cancer.
采用计算机辅助检测肺结节,首先需要快速准确的匹配分析随访前后的肺结节影像,现有的匹配方法主要包括基于全局的匹配方法和基于局部的匹配方法两类。在实际检测中,受到患者体位差异、呼吸作用等因素的影响,随访前后的肺部组织位置与状态往往不一致,导致随访前后的CT影像之间存在较大差异。由于肺部CT影像的不一致性和结节生长的不可预测性,现有的两类匹配方法的匹配准确度都比较低,严重时还会产生匹配错误。Computer-aided detection of lung nodules requires fast and accurate matching and analysis of lung nodule images before and after follow-up. The existing matching methods mainly include global-based matching methods and local-based matching methods. In actual testing, affected by factors such as differences in patient position and respiratory function, the location and state of lung tissue before and after follow-up are often inconsistent, resulting in large differences between CT images before and after follow-up. Due to the inconsistency of lung CT images and the unpredictability of nodule growth, the matching accuracy of the existing two types of matching methods are relatively low, and matching errors may occur in severe cases.
同时,现有匹配算法的计算复杂度较高,随着CT影像质量的不断提升,主流的硬件平台配置已经无法满足实际应用需求,亟需一种性能更加优异的匹配算法。At the same time, the existing matching algorithms have relatively high computational complexity. With the continuous improvement of CT image quality, mainstream hardware platform configurations can no longer meet actual application requirements, and there is an urgent need for a matching algorithm with better performance.
发明内容Summary of the invention
针对现有技术存在的问题,本发明实施例提供一种基于CT影像骨骼配准实现肺结节自适应匹配的方法与装置。In view of the problems in the prior art, embodiments of the present invention provide a method and device for adaptive matching of lung nodules based on CT image skeletal registration.
第一方面,本发明实施例提供一种基于CT影像骨骼配准实现肺结节自适应匹配的方法,其特征在于:包括以下步骤:In the first aspect, an embodiment of the present invention provides a method for adaptive matching of lung nodules based on CT image skeletal registration, which is characterized in that it includes the following steps:
(1)数据准备:准备肺部CT影像,准备肺结节数据;(1) Data preparation: prepare lung CT images and prepare lung nodules data;
(2)提取肺部与骨骼三维点云数据:在CT影像上分割肺部与骨骼区域,提取肺部和骨骼轮廓三维点云数据,并进行稀疏采样处理;(2) Extract 3D point cloud data of lungs and bones: segment lung and bone regions on CT images, extract 3D point cloud data of lungs and bone contours, and perform sparse sampling processing;
(3)三维点云数据配准:配准两组骨骼三维点云数据,利用骨骼配准得到的转换矩阵;评估两组肺部点云数据的配准误差;(3) Three-dimensional point cloud data registration: two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated;
(4)肺结节自适应匹配:基于配准误差,采用基于距离的方法匹配肺结节。(4) Adaptive matching of lung nodules: Based on the registration error, a distance-based method is used to match lung nodules.
进一步地,所述步骤(1)中进行数据准备的方法为:Further, the method for data preparation in the step (1) is:
(1.1)准备随访前后两组CT影像;(1.1) CT images of the two groups before and after preparation for follow-up;
(1.2)准备随访前后两组CT影像的结节数据,包括结节坐标,长短径,体积和属性。(1.2) The nodule data of the two groups of CT images before and after the preparation follow-up, including nodule coordinates, length and short diameter, volume and attributes.
进一步地,所述步骤(2)中提取肺部与骨骼三维点云数据的方法为:Further, the method for extracting three-dimensional point cloud data of lungs and bones in the step (2) is:
(2.1)将两组CT影像的DICOM原始数据转换为CT值数据;(2.1) Convert the DICOM raw data of the two sets of CT images into CT value data;
(2.2)将转化后的CT值数据插值到归一化空间;(2.2) Interpolate the transformed CT value data into the normalized space;
(2.3)数据重采样;(2.3) Data resampling;
(2.4)提取肺部区域;(2.4) Extract the lung area;
(2.5)根据骨骼CT值范围,提取骨骼区域,生成骨骼三维点云数据;(2.5) According to the bone CT value range, extract the bone area to generate the bone 3D point cloud data;
(2.6)根据肺部与骨骼连通区域截取数据,去掉肺部和骨骼之外的区域;(2.6) Intercept data based on the connected area of the lungs and bones, and remove the areas other than the lungs and bones;
(2.7)提取肺部和骨骼连通区域的边界轮廓;(2.7) Extract the boundary contours of the connected area between the lungs and the bones;
(2.8)将边界轮廓数据转换为三维点云数据格式。(2.8) Convert the boundary contour data into a three-dimensional point cloud data format.
进一步地,所述步骤(2.4)中提取肺部区域的方法为:Further, the method for extracting the lung area in the step (2.4) is:
(2.4.1)根据肺部组织阈值范围,初步提取连通区域;(2.4.1) Preliminarily extract connected areas according to the threshold range of lung tissue;
(2.4.2)剔除肺部组织的边界区域,填充有洞的连通区域;(2.4.2) Remove the boundary area of lung tissue and fill the connected area with holes;
(2.4.3)根据面积和位置,提取左右肺部;(2.4.3) Extract the left and right lungs according to the area and location;
(2.4.4)合并左右肺部,根据肺部位置剔除不属于肺部的连通区域。(2.4.4) Combine the left and right lungs, and exclude the connected areas that do not belong to the lungs based on the location of the lungs.
进一步地,所述步骤(3)中三维点云数据配准的方法为:Further, the method for three-dimensional point cloud data registration in the step (3) is:
(3.1)骨骼三维点云数据预处理,包括:提取骨骼三维点云数据的FPFH特征,对骨骼三维点云数据进行稀疏采样;(3.1) Preprocessing of bone 3D point cloud data, including: extracting FPFH features of bone 3D point cloud data, and sparsely sampling the bone 3D point cloud data;
(3.2)针对提取的FPFH特征和稀疏采样后的骨骼三维点云数据,采用FGR算法进行点云配准,得到变换矩阵;(3.2) For the extracted FPFH features and the sparsely sampled bone 3D point cloud data, the FGR algorithm is used for point cloud registration to obtain the transformation matrix;
(3.3)根据变换矩阵,变换移动肺部点云数据;计算变换后两组肺部点云数据的RMSE作为配准误差。(3.3) According to the transformation matrix, transform the mobile lung point cloud data; calculate the RMSE of the two sets of lung point cloud data after transformation as the registration error.
进一步地,所述步骤(4)中肺结节自适应匹配的方法为:Further, the method for adaptive matching of lung nodules in the step (4) is:
(4.1)根据变换矩阵,变换移动肺结节的坐标;(4.1) According to the transformation matrix, transform the coordinates of the moving lung nodules;
(4.2)遍历寻找并判定肺结节是否匹配;(4.2) Traverse to find and determine whether the lung nodules match;
(4.3)生成匹配结果。(4.3) Generate matching results.
进一步地,所述步骤(4)中肺结节自适应匹配的方法还包括:Further, the method for adaptive matching of lung nodules in the step (4) further includes:
根据变换后两组肺部点云数据的配准误差,自适应设定匹配结节的阈值;According to the registration error of the two sets of lung point cloud data after transformation, adaptively set the threshold of matching nodules;
对于遍历寻找并判定的肺结节,若变换移动后的肺结节坐标小于阈值;则判定该肺结节匹配成功;反之,则判定该肺结节匹配不成功。For the lung nodule searched and determined by traversal, if the coordinate of the lung nodule after transformation is less than the threshold, it is determined that the lung nodule is matched successfully; otherwise, it is determined that the lung nodule is matched unsuccessfully.
进一步地,所述基于CT影像骨骼配准实现肺结节自适应匹配的方法,还包括以下步骤:Further, the method for realizing adaptive matching of lung nodules based on CT image skeletal registration further includes the following steps:
(5)肺结节生长特性分析:针对匹配成功的肺结节,计算肺结节的长短径变化、体积变化和属性变化;针对匹配不成功的肺结节,判断该肺结节为消失或者新增。(5) Analysis of lung nodule growth characteristics: For lung nodules that are successfully matched, calculate the length and short diameter changes, volume changes, and attribute changes of the lung nodules; for lung nodules that are unsuccessfully matched, determine whether the lung nodules are disappeared or Added.
第二方面,本发明实施例提供一种电子装置,包括存储器、处理器及存储在存储器上并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如第一方面所提供的方法的步骤。In the second aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor executes the program as described in the first aspect. Steps of the provided method.
第三方面,本发明实施例提供一种非暂态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如第一方面所提供的方法的步骤。In a third aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method provided in the first aspect are implemented.
本发明实施例提供的一种基于CT影像骨骼配准实现肺结节自适应匹配的方法与装置,一是基于人体骨骼变化特性小的特点,采用随访前后CT影像中的骨骼进行配准所得到变换矩阵,对肺部影像数据和肺结节数据进行三 维点云刚性变换配准,从而实现了随访前后肺部和肺结节数据的对齐,能够有效克服肺部形变导致的误差;二是采用FGR算法,该算法不涉及迭代采样、模型拟合或局部细化,在运行速度和配准准确性方面,明显优于ICP等局部细化算法;三是采用RMSE作为肺部点云配准误差,实现了肺结节的自适应匹配,肺结节配准的人工干预少、自动化程度高,配准结果准确;四是通过对CT影像数据进行归一化处理,能够提高算法的鲁棒性,可广泛应用于不同型号的CT设备和不同pixel spacing值的DICOM数据中。The embodiment of the present invention provides a method and device for adaptive matching of lung nodules based on CT image bone registration. One is based on the characteristics of small changes in human bones and is obtained by using bones in CT images before and after follow-up for registration. The transformation matrix is used to perform three-dimensional point cloud rigid transformation registration on lung image data and lung nodule data, so as to realize the alignment of lung and lung nodule data before and after follow-up, which can effectively overcome the errors caused by lung deformation; the second is to adopt FGR algorithm, this algorithm does not involve iterative sampling, model fitting or local refinement. In terms of running speed and registration accuracy, it is significantly better than local refinement algorithms such as ICP; the third is to use RMSE as the lung point cloud registration error , Realizes the adaptive matching of lung nodules, the registration of lung nodules requires less manual intervention, high degree of automation, and accurate registration results; fourth, through the normalization of CT image data, the robustness of the algorithm can be improved It can be widely used in different types of CT equipment and DICOM data with different pixel spacing values.
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍,显而易见地,下面描述中的附图是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.
图1为本发明实施例提供的一种基于CT影像骨骼配准实现肺结节自适应匹配的方法整体流程示意图;FIG. 1 is a schematic diagram of the overall flow of a method for adaptive matching of lung nodules based on CT image bone registration according to an embodiment of the present invention;
图2为本发明实施例提供的一种CT影像骨骼点云配准前后对比示意图;2 is a schematic diagram of comparison before and after registration of a CT image bone point cloud according to an embodiment of the present invention;
图3为本发明实施例提供的一种CT影像肺部点云配准前后对比示意图;3 is a schematic diagram of comparison before and after registration of a CT image lung point cloud provided by an embodiment of the present invention;
图4为本发明实施例提供的一种CT影像肺结节匹配结果示意图;4 is a schematic diagram of a CT image lung nodule matching result provided by an embodiment of the present invention;
图5为本发明实施例提供的一种电子设备的实体结构示意图。FIG. 5 is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention.
为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.
一种基于CT影像骨骼配准实现肺结节自适应匹配的方法,如图1所示,包括以下步骤:A method for adaptive matching of lung nodules based on CT image bone registration, as shown in Figure 1, includes the following steps:
(1)数据准备:准备肺部CT影像,准备肺结节数据。具体的,所述CT影像为随访前后的两组CT影像;所述肺结节数据为CT影像所反映出的肺结节数据。(1) Data preparation: prepare lung CT images and prepare lung nodules data. Specifically, the CT images are two sets of CT images before and after follow-up; the lung nodule data is the lung nodule data reflected by the CT images.
(2)提取肺部与骨骼三维点云数据:利用医学影像处理方法,在CT影像上分割肺部与骨骼区域,提取肺部和骨骼轮廓三维点云数据,并进行稀疏采样处理。(2) Extract 3D point cloud data of lungs and bones: Using medical image processing methods, segment lung and bone regions on CT images, extract 3D point cloud data of lungs and bone contours, and perform sparse sampling processing.
(3)三维点云数据配准:配准两组骨骼三维点云数据,利用骨骼配准得到的转换矩阵;评估两组肺部点云数据的配准误差。优选的,所述配准两组骨骼三维点云数据可采用FGR(Fast Global Registration,快速全局配准部分重叠3D表面算法)或ICP(Iterative Closest Point迭代最近点算法)等算法,从而提高转换矩阵的准确度,并降低计算量。(3) Three-dimensional point cloud data registration: two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated. Preferably, the registration of the two sets of bone 3D point cloud data may use FGR (Fast Global Registration, Fast Global Registration Partially Overlapping 3D Surface Algorithm) or ICP (Iterative Closest Point Iterative Closest Point Algorithm) and other algorithms to improve the conversion matrix. Accuracy, and reduce the amount of calculation.
(4)肺结节自适应匹配:基于配准误差,采用基于距离的方法匹配肺结节。(4) Adaptive matching of lung nodules: Based on the registration error, a distance-based method is used to match lung nodules.
具体的,所述步骤(1)中进行数据准备的方法为:Specifically, the method for data preparation in the step (1) is:
(1.1)准备随访前后两组CT影像。本方法中,所述CT影像采用DICOM标准数据格式,即符合ISO 12052的国际标准数据格式,可按照DICOM tag中的ImagePosition升序排序。(1.1) CT images of the two groups before and after the preparation follow-up. In this method, the CT image adopts the DICOM standard data format, that is, the international standard data format conforming to ISO 12052, and can be sorted in ascending order according to the ImagePosition in the DICOM tag.
(1.2)准备随访前后两组CT影像的结节数据,包括结节坐标,长短径,体积和属性。本专利中,所述属性包括但不限于钙化、脂肪、坏死、密度均匀性、CT值等。(1.2) The nodule data of the two groups of CT images before and after the preparation follow-up, including nodule coordinates, length and short diameter, volume and attributes. In this patent, the attributes include but are not limited to calcification, fat, necrosis, density uniformity, CT value, etc.
具体的,所述步骤(2)中进行数据准备的方法为:Specifically, the method for data preparation in the step (2) is:
(2.1)将两组CT影像的DICOM原始数据转换为CT值数据。(2.1) Convert the DICOM raw data of the two sets of CT images into CT value data.
(2.2)将转化后的CT值数据插值到归一化空间。作为一个具体的实施方式:所述归一化空间为[2.0,2.0,2.0],将三个方向上的尺寸空间保持一致,从而提高算法泛化能力。(2.2) Interpolate the transformed CT value data into the normalized space. As a specific implementation manner: the normalized space is [2.0, 2.0, 2.0], and the size spaces in the three directions are kept consistent, thereby improving the generalization ability of the algorithm.
(2.3)数据重采样;作为一个具体的实施方式,所述重采样的区间为[0,255]。(2.3) Data resampling; as a specific implementation, the resampling interval is [0,255].
(2.4)利用形态学等方法,提取肺部区域,具体方法为:(2.4) Using morphology and other methods to extract the lung area, the specific methods are:
(2.4.1)根据肺部组织阈值范围,利用二值化方法初步提取连通区域。(2.4.1) According to the threshold range of lung tissue, the connected area is preliminarily extracted using the binarization method.
(2.4.2)剔除肺部组织的边界区域,填充有洞的连通区域。CT影像中肺部组织周围可能会出现一些不干净的区域,一般是CT拍摄时产生的,因此需要将其剔除。此外,采用二值化方法提取连通区域,会导致某些肺部产生变异的组织提取失败而产生空洞,因此需要对其进行填充。(2.4.2) Remove the boundary area of lung tissue and fill the connected area with holes. There may be some unclean areas around the lung tissue in the CT image, which are usually generated during CT imaging, so they need to be removed. In addition, the use of binarization to extract connected regions will cause some mutated lung tissues to fail to extract and produce holes, so they need to be filled.
(2.4.3)根据肺部组织的面积和位置,提取左右肺部。(2.4.3) According to the area and location of lung tissue, extract the left and right lungs.
(2.4.4)合并左右肺部,根据位置剔除不属于肺部的连通区域。实际工作中,有些数据通过上述方法,会产生部分腹部组织被提取出来的问题,因此此处还需要进行进一步的优化处理。(2.4.4) Combine the left and right lungs, and exclude the connected areas that do not belong to the lungs based on their location. In actual work, some data will be extracted through the above method, so there is a need for further optimization processing here.
(2.5)根据骨骼CT值范围,提取骨骼区域,生成骨骼三维点云数据。所述提取骨骼区域的方法,可以是基于骨骼在CT影像中的特定CT值范围进行提取,因为骨骼的CT值范围与其他器官组织是不同的。(2.5) According to the bone CT value range, the bone region is extracted to generate bone 3D point cloud data. The method for extracting the bone region may be based on the specific CT value range of the bone in the CT image, because the CT value range of the bone is different from other organs and tissues.
(2.6)根据肺部与骨骼连通区域截取数据,去掉肺部和骨骼之外的区域,从而仅保留肺部和骨骼联通区域。(2.6) The data is intercepted according to the connecting area between the lungs and the bones, and the area outside the lungs and bones is removed, so that only the connecting area between the lungs and the bones is retained.
(2.7)提取肺部和骨骼连通区域的边界轮廓。(2.7) Extract the boundary contours of the connected area between the lungs and the bones.
(2.8)将边界轮廓数据转换为三维点云数据格式。(2.8) Convert the boundary contour data into a three-dimensional point cloud data format.
具体的,所述步骤(3)中三维点云数据配准的方法为:Specifically, the method for three-dimensional point cloud data registration in the step (3) is:
(3.1)骨骼三维点云数据预处理:提取骨骼三维点云数据的FPFH(Fast Point Feature Histograms,快速点特征直方图)特征;对骨骼三维点云数据进行稀疏采样。(3.1) Preprocessing of skeleton 3D point cloud data: Extract FPFH (Fast Point Feature Histograms) features of skeleton 3D point cloud data; perform sparse sampling of skeleton 3D point cloud data.
(3.2)针对提取的FPFH特征和稀疏采样后的骨骼三维点云数据,采用FGR算法进行点云配准,得到变换矩阵。如图2所示,为本步骤CT影像骨骼点云配准前后对比示意图。从图中可以看出:左图为骨骼点云配准前的形态,配准前两个骨骼的影像不一致,无法重合;右图为骨骼点云配准后的形态,配准后两个骨骼的影像一致,基本上完全重合。(3.2) For the extracted FPFH features and the sparsely sampled bone 3D point cloud data, the FGR algorithm is used for point cloud registration, and the transformation matrix is obtained. As shown in Figure 2, this step is a schematic diagram of the comparison before and after the registration of the CT image bone point cloud. It can be seen from the figure: the left image is the morphology of the bone point cloud before registration, the images of the two bones before registration are inconsistent and cannot be overlapped; the right image is the morphology of the bone point cloud after registration, the two bones after registration The images are the same, basically completely overlapped.
(3.3)根据变换矩阵,变换移动肺部点云数据;计算变换后两组肺部点云数据的RMSE(Root Mean Square Error)作为配准误差。所述变换移动肺部点云数据的方法,是将变换矩阵与点云数据矩阵相乘。如图3所示,为本 步骤CT影像肺部点云配准前后对比示意图。从图中可以看出:左图为肺部点云配准前的形态,配准前两个肺部的影像不一致,无法重合;右图为肺部点云配准后的形态,配准后两个肺部的影像一致,基本上完全重合。(3.3) According to the transformation matrix, transform the mobile lung point cloud data; calculate the RMSE (Root Mean Square Error) of the two sets of lung point cloud data after transformation as the registration error. The method for transforming and moving lung point cloud data is to multiply the transformation matrix and the point cloud data matrix. As shown in Figure 3, this step is a schematic diagram of the comparison before and after the registration of the lung point cloud of the CT image. It can be seen from the figure: the left picture shows the shape of the lung point cloud before registration, the images of the two lungs before registration are inconsistent and cannot be overlapped; the right picture shows the shape of the lung point cloud after registration, after registration The images of the two lungs are the same, almost completely overlapped.
具体的,所述步骤(4)中肺结节自适应匹配的方法为:Specifically, the method for adaptive matching of lung nodules in the step (4) is:
(4.1)根据变换矩阵,变换移动肺结节的坐标。(4.1) According to the transformation matrix, transform the coordinates of the moving lung nodules.
(4.2)遍历寻找并判定肺结节是否匹配。(4.2) Traverse to find and determine whether the lung nodules match.
(4.3)生成匹配结果。(4.3) Generate matching results.
具体的,所述步骤(4)中肺结节自适应匹配的方法还包括:Specifically, the method for adaptive matching of lung nodules in the step (4) further includes:
根据变换后两组肺部点云数据的配准误差,自适应设定匹配结节的阈值;According to the registration error of the two sets of lung point cloud data after transformation, adaptively set the threshold of matching nodules;
所述阈值可以是根据配准误差,按照设定公式或设定规则自动计算匹配获得;通常该阈值与配准误差呈正相关性,从而相对于固定的阈值设定方式,可以针对不同的配准结果以获得更好地适应性。The threshold can be obtained by automatically calculating and matching according to the registration error according to a set formula or a set rule; usually, the threshold is positively correlated with the registration error, so that compared to a fixed threshold setting method, it can be used for different registrations. The result is better adaptability.
对于遍历寻找并判定的肺结节,若变换移动后的肺结节坐标小于阈值;则判定该肺结节匹配成功;反之,则判定该肺结节匹配不成功。For the lung nodule searched and determined by traversal, if the coordinate of the lung nodule after transformation is less than the threshold, it is determined that the lung nodule is matched successfully; otherwise, it is determined that the lung nodule is matched unsuccessfully.
如图4所示,是一次CT影像肺结节匹配结果示意图。从图中可以看出:左图为随访前CT中某一结节的位置和坐标,右图为随访CT中某一结节位置和坐标。左右两图是算法确定的配对结节,从图中可以看出,随访前后由于两次拍摄存在一定误差,结节的坐标有一定差别,但从结节所处的位置和周围组织可以判定,两个结节为不同时期的同一结节,可见本方法具有较高的匹配准确性。As shown in Figure 4, it is a schematic diagram of the matching results of a CT image of lung nodules. It can be seen from the figure: the left picture is the position and coordinates of a nodule in CT before follow-up, and the right picture is the position and coordinates of a nodule in follow-up CT. The two images on the left and right are matched nodules determined by the algorithm. It can be seen from the figure that the coordinates of the nodules are different due to the error in the two shots before and after the follow-up, but the position and surrounding tissues of the nodules can be determined. The two nodules are the same nodules in different periods, which shows that this method has higher matching accuracy.
优选的,所述基于CT影像骨骼配准实现肺结节自适应匹配的方法,还包括以下步骤:(5)肺结节生长特性分析:针对匹配成功的肺结节,计算肺结节的长短径变化、体积变化和属性变化;针对匹配不成功的肺结节,判断该肺结节为消失或者新增。通过本步骤,可以自动生成肺结节生长特性分析结果,从而为医生快速、准确做出诊断结果提供支撑。Preferably, the method for realizing adaptive matching of lung nodules based on CT image skeletal registration further includes the following steps: (5) Analysis of lung nodule growth characteristics: calculating the length of lung nodules for successfully matched lung nodules Diameter change, volume change, and attribute change; for lung nodules that are unsuccessful in matching, determine whether the lung nodule is missing or new. Through this step, the analysis results of the growth characteristics of the lung nodules can be automatically generated, so as to provide support for the doctor to make the diagnosis results quickly and accurately.
在一次实际实施过程中,通过本方法对随机选择的115例测试样本(包含601个结节,其中378个有匹配、223个无配对)开展实际测试,本方法的单个病例匹配耗时<1s,整体匹配准确率为98.5%。In an actual implementation process, 115 test samples (including 601 nodules, 378 with matching and 223 without matching) randomly selected by this method were used for actual testing. The matching time of a single case of this method was <1s , The overall matching accuracy rate is 98.5%.
本发明实施例还提供一种电子装置,如图3所示,该电子装置可以包括:处理器(processor)301、通信接口(Communications Interface)302、存储器(memory)303和通信总线304,其中,处理器301,通信接口302,存储器303通过通信总线304完成相互间的通信。处理器301可以调用存储在存储器303上并可在处理器301上运行的计算机程序,以执行上述实施例提供的方法,例如包括:(1)数据准备:准备肺部CT影像,准备肺结节数据;(2)提取肺部与骨骼点云数据:在CT影像上分割肺部与骨骼区域,提取肺部和骨骼轮廓三维点云数据,并进行稀疏采样处理;(3)三维点云数据配准:配准两组骨骼三维点云数据,利用骨骼配准得到的转换矩阵;评估两组肺部点云数据的配准误差;(4)肺结节自适应匹配:基于配准误差,采用基于距离的方法匹配肺结节;(5)肺结节生长特性分析:针对匹配成功的肺结节,计算肺结节的长短径变化、体积变化和属性变化;针对匹配不成功的肺结节,判断该肺结节为消失或者新增。An embodiment of the present invention also provides an electronic device. As shown in FIG. 3, the electronic device may include: a processor 301, a communications interface 302, a memory 303, and a communications bus 304, where: The processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. The processor 301 can call a computer program stored in the memory 303 and run on the processor 301 to execute the method provided in the above embodiment, for example, including: (1) Data preparation: preparing lung CT images, preparing lung nodules Data; (2) Extracting lung and bone point cloud data: segmenting lung and bone regions on CT images, extracting 3D point cloud data of lung and bone contours, and performing sparse sampling processing; (3) 3D point cloud data matching Accurate: register two sets of bone 3D point cloud data, use the conversion matrix obtained by bone registration; evaluate the registration error of the two sets of lung point cloud data; (4) adaptive matching of lung nodules: based on the registration error, use Distance-based method to match lung nodules; (5) Analysis of lung nodule growth characteristics: calculate the length and short diameter changes, volume changes and attribute changes of lung nodules for successfully matched lung nodules; for unsuccessful lung nodules , It is judged that the lung nodule is disappeared or newly added.
此外,上述的存储器303中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时,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,本发明实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案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)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。In addition, the above-mentioned logical instructions in the memory 303 can be implemented in the form of a software functional unit and when sold or used as an independent product, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention can be embodied in the form of software products in essence or parts that contribute to the prior art or parts of the technical solutions, and the computer software products are stored in a storage medium. , Including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
本发明实施例还提供一种非暂态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执行上述各实施例提供的方法,例如包括:(1)数据准备:准备肺部CT影像,准备肺结节数据;(2)提取肺部与骨骼点云数据:在CT影像上分割肺部与骨骼区域,提取肺部和骨骼轮廓三维点云数据,并进行稀疏采样处理;(3)三维点云数据配准:配准两组骨骼三维点云数据,利用骨骼配准得到的转换矩阵;评估两组肺部点云 数据的配准误差;(4)肺结节自适应匹配:基于配准误差,采用基于距离的方法匹配肺结节;(5)肺结节生长特性分析:针对匹配成功的肺结节,计算肺结节的长短径变化、体积变化和属性变化;针对匹配不成功的肺结节,判断该肺结节为消失或者新增。The embodiment of the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. The computer program is implemented when executed by a processor to perform the methods provided in the foregoing embodiments, for example, including: (1) Data Preparation: prepare lung CT images, prepare lung nodules data; (2) extract lung and bone point cloud data: segment lung and bone regions on CT images, extract lung and bone contour 3D point cloud data, and proceed Sparse sampling processing; (3) Three-dimensional point cloud data registration: two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated; (4) Lung Nodule adaptive matching: Based on the registration error, a distance-based method is used to match lung nodules; (5) Analysis of lung nodule growth characteristics: For lung nodules that are successfully matched, calculate the length and short diameter changes and volume changes of the lung nodules And attribute changes; for lung nodules that are unsuccessful in matching, judge the lung nodules as disappeared or newly added.
以上所描述的装置实施例仅仅是示意性的,其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者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者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下,即可以理解并实施。The device embodiments described above are merely illustrative, where the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement it without creative work.
通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件。基于这样的理解,上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品可以存储在计算机可读存储介质中,如ROM/RAM、磁碟、光盘等,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)执行各个实施例或者实施例的某些部分所述的方法。Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic A disc, an optical disc, etc., include several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiment.
最后应说明的是:以上实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the foregoing embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (3)
- 一种基于CT影像骨骼配准实现肺结节自适应匹配的方法,其特征在于:包括以下步骤:A method for realizing adaptive matching of lung nodules based on CT image bone registration, which is characterized in that it includes the following steps:(1)数据准备:准备肺部CT影像,准备肺结节数据;(1) Data preparation: prepare lung CT images and prepare lung nodules data;(2)提取肺部与骨骼点云数据:在CT影像上分割肺部与骨骼区域,提取肺部和骨骼轮廓三维点云数据,并进行稀疏采样处理;(2) Extracting lung and bone point cloud data: segmenting lung and bone regions on CT images, extracting three-dimensional point cloud data of lung and bone contours, and performing sparse sampling processing;(3)三维点云数据配准:配准两组骨骼三维点云数据,利用骨骼配准得到的转换矩阵;评估两组肺部点云数据的配准误差;(3) Three-dimensional point cloud data registration: two sets of bone three-dimensional point cloud data are registered, and the conversion matrix obtained by bone registration is used; the registration error of the two sets of lung point cloud data is evaluated;(4)肺结节自适应匹配:基于配准误差,采用基于距离的方法匹配肺结节;(4) Adaptive matching of lung nodules: Based on the registration error, a distance-based method is used to match lung nodules;所述步骤(1)中进行数据准备的方法为:The method for data preparation in the step (1) is:(1.1)准备随访前后两组CT影像;(1.1) CT images of the two groups before and after preparation for follow-up;(1.2)准备随访前后两组CT影像的结节数据,包括结节坐标,长短径,体积和属性;所述步骤(2)中提取肺部与骨骼点云数据的方法为:(1.2) Preparation of the nodule data of the two groups of CT images before and after follow-up, including nodule coordinates, length and diameter, volume and attributes; the method of extracting lung and bone point cloud data in step (2) is:(2.1)将两组CT影像的DICOM原始数据转换为CT值数据;(2.1) Convert the DICOM raw data of the two sets of CT images into CT value data;(2.2)将转化后的CT值数据插值到归一化空间;(2.2) Interpolate the transformed CT value data into the normalized space;(2.3)数据重采样;(2.3) Data resampling;(2.4)提取肺部区域;(2.4) Extract the lung area;(2.5)根据骨骼CT值范围提取骨骼区域,生成骨骼三维点云数据;(2.5) Extract the bone region according to the bone CT value range, and generate the bone 3D point cloud data;(2.6)根据肺部与骨骼连通区域截取数据,去掉肺部和骨骼之外的区域;(2.6) Intercept data based on the connected area of the lungs and bones, and remove the areas other than the lungs and bones;(2.7)提取肺部和骨骼连通区域的边界轮廓;(2.7) Extract the boundary contours of the connected area between the lungs and the bones;(2.8)将边界轮廓数据转换为三维点云数据格式;所述步骤(2.4)中提取肺部区域的方法为:(2.8) Convert the boundary contour data into a three-dimensional point cloud data format; the method of extracting the lung area in the step (2.4) is:(2.4.1)根据肺部组织阈值范围,初步提取连通区域;(2.4.1) Preliminarily extract connected areas according to the threshold range of lung tissue;(2.4.2)剔除肺部组织的边界区域,填充有洞的连通区域;(2.4.2) Remove the boundary area of lung tissue and fill the connected area with holes;(2.4.3)根据面积和位置,提取左右肺部;(2.4.3) Extract the left and right lungs according to the area and location;(2.4.4)合并左右肺部,根据肺部位置剔除不属于肺部的连通区域;所述 步骤(3)中三维点云数据配准的方法为:(2.4.4) Combine the left and right lungs, and exclude the connected areas that do not belong to the lungs based on the location of the lungs; the three-dimensional point cloud data registration method in step (3) is:(3.1)骨骼三维点云数据预处理,包括:提取骨骼三维点云数据的FPFH特征,对骨骼三维点云数据进行稀疏采样;(3.1) Preprocessing of bone 3D point cloud data, including: extracting FPFH features of bone 3D point cloud data, and sparsely sampling the bone 3D point cloud data;(3.2)针对提取的FPFH特征和稀疏采样后的骨骼三维点云数据,采用FGR算法进行点云配准,得到变换矩阵;(3.2) For the extracted FPFH features and the sparsely sampled bone 3D point cloud data, the FGR algorithm is used for point cloud registration to obtain the transformation matrix;(3.3)根据变换矩阵,变换移动肺部点云数据;计算变换后两组肺部点云数据的RMSE作为配准误差;(3.3) According to the transformation matrix, transform the mobile lung point cloud data; calculate the RMSE of the two sets of lung point cloud data after transformation as the registration error;所述步骤(4)中肺结节自适应匹配的方法为:The method for adaptive matching of lung nodules in the step (4) is:(4.1)根据变换矩阵,变换移动肺结节的坐标;(4.1) According to the transformation matrix, transform the coordinates of the moving lung nodules;(4.2)遍历寻找并判定肺结节是否匹配;(4.2) Traverse to find and determine whether the lung nodules match;(4.3)生成匹配结果。(4.3) Generate matching results.
- 根据权利要求1所述的基于CT影像骨骼配准实现肺结节自适应匹配的方法,其特征在于:所述步骤(4)中肺结节自适应匹配的方法还包括:The method for realizing adaptive matching of lung nodules based on CT image bone registration according to claim 1, wherein the method for adaptive matching of lung nodules in the step (4) further comprises:根据变换后两组肺部点云数据的配准误差,自适应设定匹配结节的阈值;According to the registration error of the two sets of lung point cloud data after transformation, adaptively set the threshold of matching nodules;对于遍历寻找并判定的肺结节,若变换移动后的肺结节坐标小于阈值;则判定该肺结节匹配成功;反之,则判定该肺结节匹配不成功。For the lung nodule searched and determined by traversal, if the coordinate of the lung nodule after transformation is less than the threshold, it is determined that the lung nodule is matched successfully; otherwise, it is determined that the lung nodule is matched unsuccessfully.
- 根据权利要求2所述的基于CT影像骨骼配准实现肺结节自适应匹配的方法,其特征在于:还包括以下步骤:The method for realizing adaptive matching of lung nodules based on CT image bone registration according to claim 2, characterized in that it further comprises the following steps:(5)肺结节生长特性分析:针对匹配成功的肺结节,计算肺结节的长短径变化、体积变化和属性变化;针对匹配不成功的肺结节,判断该肺结节为消失或者新增。(5) Analysis of lung nodule growth characteristics: For lung nodules that are successfully matched, calculate the length and short diameter changes, volume changes, and attribute changes of the lung nodules; for lung nodules that are unsuccessfully matched, determine whether the lung nodules are disappeared or Added.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114372227A (en) * | 2021-12-31 | 2022-04-19 | 浙江大学舟山海洋研究中心 | Intelligent squid white slice cutting calculation method, device, equipment and storage medium |
CN114897703A (en) * | 2022-06-08 | 2022-08-12 | 西安交通大学 | Automatic splicing system and method for orthopedic full-length X-ray films, computer equipment and storage medium |
US11832621B1 (en) | 2021-12-31 | 2023-12-05 | Ocean Research Center Of Zhoushan, Zhejiang University | Methods and systems for intelligent processing of aquatic products |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112288786B (en) * | 2019-12-06 | 2022-03-22 | 广州柏视医疗科技有限公司 | Electronic device and non-transitory computer-readable storage medium |
CN112258449A (en) * | 2020-09-15 | 2021-01-22 | 深圳视见医疗科技有限公司 | Rapid nodule matching method based on nodule characteristics |
CN113570648B (en) * | 2021-07-30 | 2023-09-26 | 武汉联影智融医疗科技有限公司 | Multi-skeleton image registration method, electronic device and medical navigation system |
CN116309248B (en) * | 2022-09-07 | 2024-06-25 | 拓微摹心数据科技(南京)有限公司 | Automatic evaluation method for calcification spatial distribution uniformity of aortic root |
CN115880469B (en) * | 2023-02-20 | 2023-05-09 | 江苏省人民医院(南京医科大学第一附属医院) | Registration method of surface point cloud data and three-dimensional image |
CN117455905B (en) * | 2023-12-20 | 2024-03-22 | 深圳大学 | Intelligent detection method and system for modularized integrated building component |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2006057724A2 (en) * | 2004-11-29 | 2006-06-01 | The Board Of Trustees Of The Leland Stanford Junior University | A registration system and method for tracking lung nodules in medical images |
CN109009435A (en) * | 2018-08-20 | 2018-12-18 | 真健康(北京)医疗科技有限公司 | A kind of Lung neoplasm puncture navigation system |
CN109816655A (en) * | 2019-02-01 | 2019-05-28 | 华院数据技术(上海)有限公司 | Lung neoplasm image feature detection method based on ct images |
CN111127527A (en) * | 2019-12-06 | 2020-05-08 | 广州柏视医疗科技有限公司 | Method and device for realizing lung nodule self-adaptive matching based on CT image bone registration |
Family Cites Families (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2002056240A1 (en) * | 2000-11-22 | 2002-07-18 | R2 Technology, Inc. | Graphical user interface for display of anatomical information |
US7499578B2 (en) * | 2002-10-18 | 2009-03-03 | Cornell Research Foundation, Inc. | System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans |
US7486812B2 (en) * | 2003-11-25 | 2009-02-03 | Icad, Inc. | Shape estimates and temporal registration of lesions and nodules |
CN102411781B (en) * | 2011-09-09 | 2013-11-13 | 华南理工大学 | Motion correction system for dual-energy subtraction chest X-ray image |
KR101909125B1 (en) * | 2011-11-23 | 2018-10-18 | 삼성전자주식회사 | Method for computer-aided diagnosis and computer-aided diagnosis apparatus thereof |
CN103035009A (en) * | 2012-12-19 | 2013-04-10 | 长春工业大学 | Pulmonary nodule edge rebuilding and partitioning method based on computed tomography (CT) image |
CN104021547B (en) * | 2014-05-17 | 2017-02-15 | 清华大学深圳研究生院 | Three dimensional matching method for lung CT |
DK3286727T3 (en) * | 2014-11-07 | 2019-09-23 | Antaros Medical Ab | WHOLE BODY IMAGE REGISTRATION METHOD AND METHOD OF ANALYZING IMAGES THEREOF |
JP6363575B2 (en) * | 2015-09-29 | 2018-07-25 | 富士フイルム株式会社 | Image alignment apparatus and method, and program |
CN105913442A (en) * | 2016-04-29 | 2016-08-31 | 上海联影医疗科技有限公司 | Method for automatically matching pulmonary nodules |
WO2018132804A1 (en) * | 2017-01-16 | 2018-07-19 | Lang Philipp K | Optical guidance for surgical, medical, and dental procedures |
US10182771B2 (en) * | 2017-02-10 | 2019-01-22 | Siemens Healthcare Gmbh | Method and system for dose-optimized computed tomography scanning of a target organ |
US10621720B2 (en) * | 2017-04-27 | 2020-04-14 | Siemens Healthcare Gmbh | Deformable registration of magnetic resonance and ultrasound images using biomechanical models |
CN107507189A (en) * | 2017-07-04 | 2017-12-22 | 西北大学 | Mouse CT image kidney dividing methods based on random forest and statistical model |
CN108717707A (en) * | 2018-04-10 | 2018-10-30 | 杭州依图医疗技术有限公司 | A kind of tubercle matching process and device |
CN110215281B (en) * | 2019-06-11 | 2020-07-10 | 北京和华瑞博医疗科技有限公司 | Femur or tibia registration method and device based on total knee replacement surgery |
CN111462201B (en) * | 2020-04-07 | 2023-04-18 | 广州柏视医疗科技有限公司 | Follow-up analysis system and method based on novel coronavirus pneumonia CT image |
-
2019
- 2019-12-06 CN CN202011337226.2A patent/CN112288786B/en active Active
- 2019-12-06 CN CN201911238297.4A patent/CN111127527B/en active Active
-
2020
- 2020-12-01 WO PCT/CN2020/133091 patent/WO2021109988A1/en active Application Filing
- 2020-12-01 WO PCT/CN2020/133090 patent/WO2021109987A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2006057724A2 (en) * | 2004-11-29 | 2006-06-01 | The Board Of Trustees Of The Leland Stanford Junior University | A registration system and method for tracking lung nodules in medical images |
CN109009435A (en) * | 2018-08-20 | 2018-12-18 | 真健康(北京)医疗科技有限公司 | A kind of Lung neoplasm puncture navigation system |
CN109816655A (en) * | 2019-02-01 | 2019-05-28 | 华院数据技术(上海)有限公司 | Lung neoplasm image feature detection method based on ct images |
CN111127527A (en) * | 2019-12-06 | 2020-05-08 | 广州柏视医疗科技有限公司 | Method and device for realizing lung nodule self-adaptive matching based on CT image bone registration |
Non-Patent Citations (2)
Title |
---|
HONG, H. ; LEE, J. ; YIM, Y.: "Automatic lung nodule matching on sequential CT images", COMPUTERS IN BIOLOGY AND MEDICINE, NEW YORK, NY, US, vol. 38, no. 5, 1 May 2008 (2008-05-01), US, pages 623 - 634, XP022683881, ISSN: 0010-4825, DOI: 10.1016/j.compbiomed.2008.02.010 * |
ZHANG CHUNSHENG: "Non-Rigid Registration of Lung Surfaces Based on CT Images", CHINESE DOCTORAL DISSERTATIONS FULL-TEXT DATABASE (ELECTRONIC JOURNAL), 1 July 2012 (2012-07-01), XP055818954 * |
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CN111127527A (en) | 2020-05-08 |
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