CN112967291A

CN112967291A - Image processing method and device, electronic equipment and storage medium

Info

Publication number: CN112967291A
Application number: CN202110226182.4A
Authority: CN
Inventors: 李元杰; 隋雨桐; 吴振洲; 刘盼
Original assignee: Beijing Ande Yizhi Technology Co ltd
Current assignee: Beijing Ande Yizhi Technology Co ltd
Priority date: 2021-03-01
Filing date: 2021-03-01
Publication date: 2021-06-15
Anticipated expiration: 2041-03-01
Also published as: CN112967291B; WO2021259391A2; WO2021259391A3

Abstract

The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium, the method including: performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a focus region corresponding to a focus on the target organ; according to the positions of the organ area and the focus area, determining an abnormal subarea which can correspond to the part bearing the focus organ from the organ area; determining a focus interface between the abnormal subarea and the focus area according to the position relation between a first pixel point in the abnormal subarea and a second pixel point in the focus area; and determining the morphological analysis result of the focus according to the focus interface and the focus area. According to the embodiment of the disclosure, the morphological analysis result of the focus in the image can be automatically and accurately determined, so that the workload of medical staff is reduced, and the working efficiency of the medical staff is improved.

Description

Image processing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer vision technologies, and in particular, to an image processing method and apparatus, an electronic device, and a storage medium.

Background

In the field of computer vision technology, the application of image recognition and analysis is very wide, for example, in medical imaging, the recognition and analysis of a lesion region is the basis of disease diagnosis.

In the related art, there are two types of methods for identifying and morphologically analyzing a lesion region in a medical image.

Firstly, the medical image is interpreted by a doctor to identify and analyze the focus area, and the doctor can manually measure the focus area to obtain the morphological parameters of the focus area and give a diagnosis result. However, when doctors manually measure the lesion area, the consistency of measurement varies, and the diagnosis level and clinical experience of doctors are relatively depended, and even some doctors may have misdiagnosis and missed diagnosis.

Secondly, the identification and analysis of the lesion area with the aid of a computer require a doctor to assist in determining certain characteristic points and characteristic areas in the medical image, for example, calculating morphological parameters of an aneurysm in the medical image and determining a tumor neck point or a tumor neck plane. However, manual error is easily introduced during the physician-assisted procedure.

Disclosure of Invention

In view of the above, the present disclosure provides an image processing method and apparatus, an electronic device, and a storage medium.

According to an aspect of the present disclosure, there is provided an image processing method, the method including: performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a focus region corresponding to a focus on the target organ; determining an abnormal subarea from the organ area according to the positions of the organ area and the focus area, wherein the abnormal subarea corresponds to an organ part bearing the focus; determining a focus interface between the abnormal subarea and the focus area according to the position relation between a first pixel point in the abnormal subarea and a second pixel point in the focus area; and determining a morphological analysis result of the focus according to the focus interface and the focus area.

In a possible implementation manner, determining a lesion interface between the abnormal sub-region and the lesion region according to a positional relationship between a first pixel point in the abnormal sub-region and a second pixel point in the lesion region includes: determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point; and determining a focus interface from the plurality of reference planes according to the number of boundary reference points in the plurality of preset reference planes, wherein the plurality of reference planes are respectively vertical to all coordinate axes of the image coordinate system of the image to be processed.

In a possible implementation manner, determining a boundary reference point from the first pixel point and the second pixel point according to a distance between the first pixel point and the second pixel point includes: determining a first distance between the first pixel point and each second pixel point in the focal region aiming at any first pixel point; under the condition that a first distance smaller than or equal to a distance threshold exists, determining the first pixel point as a boundary reference point; determining a second distance between the second pixel point and each first pixel point in the abnormal subarea aiming at any second pixel point; and under the condition that a second distance smaller than or equal to the distance threshold exists, determining the second pixel point as a boundary reference point.

In one possible implementation, determining an abnormal sub-region from the organ region according to the positions of the organ region and the lesion region includes: acquiring a first space region which is externally connected with the focus region; expanding the first space region according to a preset expansion coefficient to obtain an expanded second space region; and determining a spatial region which belongs to the organ region and does not belong to the focus region in the second spatial region as the abnormal subregion.

In one possible implementation, the determining the morphological analysis result of the lesion according to the lesion interface and the lesion region includes: determining the distance between two boundary reference points with the largest distance as the reference diameter of the focus in the boundary reference points included in the focus interface; determining the maximum distance between a second pixel point and the geometric center of the focus interface as the maximum diameter of the focus in a second pixel point included in the focus area; determining the distance between two second pixel points with the largest distance as the width of the focus in the direction vertical to the maximum diameter in the second pixel points included in the focus area; and determining the maximum distance between the second pixel point and the focus interface as the height of the focus in a second pixel point included in the focus area.

In one possible implementation manner, performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a lesion region corresponding to a lesion on the target organ includes: normalizing the image to be processed to obtain a processed first image; performing first segmentation processing on the first image to determine an organ region in the first image; and performing second segmentation processing on the first image to determine a lesion area in the first image.

In one possible implementation, performing a first segmentation process on the first image to determine an organ region in the first image includes: cutting the first image according to a first preset size to obtain a first sampling image block; inputting the first sampling image block into a first segmentation network for segmentation to obtain a segmentation result of the first sampling image block; fusing the segmentation results of the plurality of first sampling image blocks to obtain an organ area in the first image;

performing a second segmentation process on the first image to determine a lesion region in the first image, including: cutting the first image according to a second preset size to obtain a second sampling image block; inputting the second sampling image block into a second segmentation network for segmentation to obtain a segmentation result of the second sampling image block; and fusing the segmentation results of the plurality of second sampling image blocks to obtain a focus area in the first image.

In one possible implementation, the image to be processed includes a three-dimensional angiographic image, the target organ includes a blood vessel, the lesion on the target organ includes an aneurysm, the anomaly region includes a parent artery region, and the lesion interface includes a neck plane of the aneurysm.

According to an aspect of the present disclosure, there is provided an image processing apparatus including: the segmentation module is used for performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a focus region corresponding to a focus on the target organ; an abnormal sub-region determining module, configured to determine an abnormal sub-region from the organ region according to the positions of the organ region and the lesion region, where the abnormal sub-region corresponds to an organ portion bearing the lesion; the focus interface determining module is used for determining a focus interface between the abnormal subregion and the focus region according to the position relationship between a first pixel point in the abnormal subregion and a second pixel point in the focus region; and the focus analysis module is used for determining a morphological analysis result of the focus according to the focus interface and the focus area.

According to another aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

In the embodiment of the disclosure, an organ region and a focus region are determined by segmenting an image to be processed, and a focus interface is determined according to the position relationship between the organ region and the focus region; and finally, determining a morphological analysis result of the focus according to the focus interface and the focus area. The method can automatically and accurately determine the morphological analysis result of the focus in the image, thereby reducing the workload of medical staff and improving the working efficiency of the medical staff.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 shows a flow diagram of an image processing method according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of an anomaly region in accordance with an embodiment of the present disclosure;

fig. 3 shows a schematic diagram of a morphological analysis result of a lesion according to an embodiment of the present disclosure;

fig. 4 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of an electronic device according to an embodiment of the disclosure;

fig. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Fig. 1 illustrates a flowchart of an image processing method according to an embodiment of the present disclosure, which includes, as illustrated in fig. 1:

in step S11, performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a lesion region corresponding to a lesion on the target organ;

in step S12, determining an abnormal subregion from the organ region according to the positions of the organ region and the lesion region, wherein the abnormal subregion corresponds to an organ part bearing the lesion;

in step S13, determining a lesion interface between the abnormal sub-region and the lesion region according to a positional relationship between a first pixel point in the abnormal sub-region and a second pixel point in the lesion region;

in step S14, a morphological analysis result of the lesion is determined based on the lesion boundary and the lesion region.

In a possible implementation manner, the image processing method may be executed by an electronic device such as a terminal device or a server, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a terminal, and the like, and the other processing device may be a server or a cloud server, and the like. In some possible implementations, the image processing method may be implemented by a processor calling computer readable instructions stored in a memory. Alternatively, the method may be performed by a server.

In one possible implementation, the image to be processed may be a medical image, which may be an image taken by various types of medical equipment, or an image used for medical diagnosis, such as a Computed Tomography (CT) image or a Magnetic Resonance Imaging (MRI) image. The present disclosure does not limit the type of image to be processed and the specific acquisition manner.

In one possible implementation, the image to be processed may be a three-dimensional medical image, including, for example, a three-dimensional angiographic image. In order to describe the position of an organ, a tissue or a lesion in an image to be processed in clinic, a Coronal View (Coronal View), a Sagittal View (Sagittal View) and an Axial View (Axial View) can be set on a three-dimensional medical image;

the coronal position may represent the orientation of the human body longitudinally cut into the front and rear parts along the long axis of the human body, the sagittal position may represent the orientation of the human body longitudinally cut into the left and right parts along the long axis of the human body, and the axial position may represent the orientation of the human body transversely cut into the upper and lower parts along the horizontal direction. The coronal, sagittal, and axial positions may correspond to the orientation of coordinate axes in a rectangular coordinate system formed by xyz.

In one possible implementation, the image to be processed includes a target organ and a lesion on the target organ, for example, the target organ of the image to be processed may be an intracranial blood vessel, a cardiac coronary artery, a pulmonary artery, etc., and the lesion on the target organ may be an intracranial cystic aneurysm, a coronary aneurysm, a pulmonary aneurysm, etc., and the disclosure does not limit the specific target organ and the lesion on the target organ. There may be one or more lesions on the target organ, and the number of lesions on the target organ is not limited by the present disclosure.

In one possible implementation, the image to be processed may be preprocessed before being segmented, so as to facilitate the subsequent image segmentation processing. The preprocessing includes unifying a resolution of a physical space (Spacing) of an image to be processed, unifying a value range of a pixel value in the image to be processed, performing region cropping on the image to be processed, and the like. By the method, the size of the image can be unified, the data volume to be processed is reduced, and the subsequent image segmentation operation is facilitated. The present disclosure does not limit the specific content of the pretreatment and the treatment manner.

In one possible implementation, in step S11, a segmentation process may be performed on an image to be processed, and an organ region corresponding to a target organ in the image to be processed and a lesion region corresponding to a lesion on the target organ may be determined; wherein the target organ comprises, for example, a blood vessel and the lesion on the target organ comprises, for example, an aneurysm.

For example, the image to be processed is a three-dimensional medical image, which may include a target organ and one or more lesions on the target organ. By performing segmentation processing on the image to be processed, a segmentation result including an organ region corresponding to the target organ in the image to be processed and a lesion region corresponding to a lesion on the target organ may be obtained. For example, the image to be processed is an intracranial angiography image, and the image is subjected to segmentation processing, so that a segmentation result can be obtained, wherein the segmentation result comprises a blood vessel region in the intracranial angiography image and a lesion region where one or more aneurysms on the blood vessel are located.

In a possible implementation manner, since the target organ and the lesion on the target organ are in an adhesion state on the image to be processed, an overlap region may exist between the organ region and the lesion region determined after the segmentation processing, that is, one or more pixel points may exist, and belong to both the organ region and the lesion region.

In a possible implementation manner, during the process of performing segmentation processing on the image to be processed, a segmentation result can be obtained through one segmentation processing. In the segmentation result, each pixel in the organ region may be labeled as 1, each pixel in the focal region carried on the organ may be labeled as 2, and each pixel in the other background region may be labeled as 0. By performing segmentation processing on the image to be processed once, an organ region and a focus region can be rapidly obtained.

Alternatively, in the process of performing segmentation processing on the image to be processed, a segmentation result can be obtained by performing segmentation processing twice. That is, a first segmentation result may be obtained by performing a first segmentation process on the image to be processed, and in the first segmentation result, each pixel in the organ region may be labeled as 1, and each pixel in the background region outside the organ region may be labeled as 0. From the first segmentation result, the organ region of the image to be processed may be determined. A second segmentation result may be obtained by performing a second segmentation process on the image to be processed, where in the second segmentation result, each pixel in a focal region borne by the target organ may be marked as 1, and each pixel in a background region outside the focal region may be marked as 0. According to the second segmentation result, a lesion region of the image to be processed may be determined. By carrying out two times of segmentation processing on the image to be processed, the information of the image to be processed can be fully utilized, and a more accurate segmentation result can be obtained.

It should be understood that the present disclosure may perform the first segmentation processing and the second segmentation processing on the image to be processed in parallel, or may perform the second segmentation processing on the image to be processed first and then perform the first segmentation processing, and the present disclosure does not limit the order of the two segmentation processing and the segmentation processing manner.

In one possible implementation manner, in step S12, an abnormal sub-region is determined from the organ region according to the positions of the organ region and the lesion region, and the abnormal sub-region corresponds to the part of the organ bearing the lesion. Wherein the abnormal subarea comprises a parent artery area, for example.

For example, since the organ region includes a large amount of pixel point data, in the process of obtaining the morphological analysis result of the lesion according to the organ region and the lesion region, the morphological analysis result of the lesion is not affected by the pixel point data of a part of the organ region far from the lesion region.

In order to reduce the amount of data calculation and improve the calculation efficiency, an abnormal sub-region which can influence the morphological analysis result of the focus can be determined from the organ region according to the position relationship between the organ region and the focus region, wherein the abnormal sub-region corresponds to the part of the organ bearing the focus, namely the part of the sub-region adjacent to the focus region in the organ region. In order to improve the accuracy of the morphological analysis result of the focus, the overlapping region in the organ region and the focus region can be removed in the process of determining the abnormal subregion from the organ region according to the positions of the organ region and the focus region.

Wherein each focus in the focus area corresponds to one abnormal subarea, and the organ area can comprise one or more abnormal subareas. The abnormal subareas corresponding to all the focuses can be determined one by one, and the abnormal subareas corresponding to all the focuses can also be determined in parallel, which is not limited by the disclosure.

In one possible implementation manner, in step S13, a lesion interface between the abnormal sub-region and the lesion region is determined according to a position relationship between a first pixel point in the abnormal sub-region and a second pixel point in the lesion region. Wherein the focal interface comprises, for example, a tumor neck plane of the aneurysm.

For example, a lesion interface between the abnormal sub-region and the lesion region is determined according to the abnormal sub-region and the lesion region, and the lesion interface between the abnormal sub-region and the lesion region may be determined according to a positional relationship between a first pixel point included in the abnormal sub-region and a second pixel point included in the lesion region. For example, the abnormal sub-region is a tumor-carrying vessel region in the vessel region, the lesion region is an aneurysm region, and a lesion interface between the tumor-carrying vessel region and the aneurysm region, that is, a tumor neck plane, can be determined according to a positional relationship between a first pixel point in the tumor-carrying vessel region and a second pixel point in the aneurysm region.

In one possible implementation manner, in step S14, a morphological analysis result of the lesion is determined according to the lesion interface and the lesion region.

For example, the morphological analysis result of the lesion may be determined according to the above steps to obtain the lesion interface and the lesion area. That is, the morphological analysis result of the lesion area may be determined according to the positional relationship between the boundary reference point in the lesion interface and the second pixel point in the lesion area. Wherein, the morphological analysis result of the lesion may include a plurality of morphological parameters of the lesion, such as a reference diameter, a maximum diameter, a width, a height, and the like of the lesion.

By the method, the morphological analysis result of the focus can be automatically determined without the assistance of a doctor, so that errors caused by manual intervention of the doctor can be avoided, the accuracy of the morphological analysis result of the focus is improved, the workload of the doctor is reduced, and the working efficiency of medical staff is improved.

The following is a description of an image processing method according to an embodiment of the present disclosure.

In one possible implementation, step S11 may include: normalizing the image to be processed to obtain a processed first image; performing first segmentation processing on the first image to determine an organ region in the first image; and performing second segmentation processing on the first image to determine a lesion area in the first image.

For example, the image to be processed is normalized, that is, the pixel value of each pixel in the image to be processed is normalized to be within the range of 0-1 value range, so as to improve the processing efficiency. For example, assuming that the image to be processed is an 8-bit grayscale image, and the pixel value range of each pixel is 0-255, the pixel value of each pixel can be divided by 255, so that the pixel value of each pixel in the image to be processed is normalized to be within the range of 0-1. After the normalization processing is performed on the image to be processed, a first image can be obtained. It is understood that the normalization method may include, but is not limited to, linear function normalization (Min-Max Scaling), 0-mean normalization (Z-Score normalization), non-linear normalization, and the like, and the normalization method is not limited by the present disclosure.

In a possible implementation manner, after obtaining the normalized first image, the first image may be subjected to a first segmentation process, and a first segmentation result may be obtained, where the result includes an organ region where the target organ is located and a background region outside the organ region. For example, the first image is a normalized intracranial Angiography image (CT Angiography, CTA), and the image may be subjected to a first segmentation process to obtain a first segmentation result, that is, a blood vessel region in the intracranial Angiography image and a background region outside the blood vessel region. The first segmentation result may be a binary label, that is, a blood vessel region in the intracranial angiography image is labeled as 1, and a background region outside the blood vessel region is labeled as 0.

In a possible implementation manner, a first segmentation network may be preset, and is configured to perform a first segmentation process on the first image and determine an organ region where the target organ is located in the first image. The first segmentation Network may be a deep convolutional neural Network, and includes a plurality of convolutional layers, a plurality of deconvolution layers, a full connection layer, and the like, and the specific segmentation Network that may be used includes, but is not limited to, Network structures such as a U-Network (U-NET), a V-Network (V-NET), and the like.

The first image may be subjected to a second segmentation process with reference to the first segmentation process method, so as to obtain a second segmentation result, where the second segmentation result includes a lesion region where a lesion on the target organ is located and a background region outside the lesion region. For example, the first image is a normalized intracranial Angiography image (CT Angiography, CTA), and the image may be subjected to a second segmentation process to obtain a second segmentation result, that is, an aneurysm region on a blood vessel region in the intracranial Angiography image and a background region outside the aneurysm region; the second segmentation result may be a binary label, that is, an aneurysm region in the intracranial angiography image is labeled as 1, and a background region outside the blood vessel region is labeled as 0.

In a possible implementation manner, a second segmentation network may be preset, and is configured to perform a second segmentation process on the second image to determine a lesion region where a lesion on the target organ is located in the second image. The second segmentation Network may be a deep convolutional neural Network, and includes a plurality of convolutional layers, a plurality of deconvolution layers, a full connection layer, and the like, and the specific segmentation networks that may be used include, but are not limited to, Network structures such as a U-Network (U-NET), a V-Network (V-NET), and the like.

In this way, the organ region and the lesion region in the first image can be automatically segmented without the assistance of a physician.

In one possible implementation manner, the performing a first segmentation process on the first image in step S11 to determine an organ region in the first image includes: cutting the first image according to a first preset size to obtain a first sampling image block; inputting the first sampling image block into a first segmentation network for segmentation to obtain a segmentation result of the first sampling image block; fusing the segmentation results of the plurality of first sampling image blocks to obtain an organ area in the first image;

for example, during the first segmentation process of the first image, a first preset size may be set, so that sizes of first sample image blocks input to the first segmentation network are consistent. For example, assume that the first image has a size of 256 × 512 × 512, i.e., 256 pixels in the z-axis direction (i.e., the direction of the slice pitch of the medical image), and 512 pixels in the x-axis (width) direction and the y-axis (height) direction, respectively. The first preset size may be set to 64 × 384 × 384, that is, 64 pixels in the z-axis direction and 384 pixels in the x-axis direction and the y-axis direction, respectively.

The first image may be cut with overlap in the x-axis direction, the y-axis direction, and the z-axis direction according to a certain fixed cutting step length to obtain a plurality of cut image blocks of a first preset size 64 × 384 × 384, where each cut image block is a first sample image block. And partial image areas of the adjacent first sampling image blocks are overlapped. The number of the first sampling image blocks and the size of the overlapping area of each first sampling image block can be determined according to a first preset size and a cutting step length. The present disclosure does not limit the first preset size and the cutting step size.

The plurality of first sample image blocks are input into the first division network for processing, and division results of the plurality of first sample image blocks can be obtained. According to the cutting position of each first sampling image block, a plurality of first sampling image blocks can be fused to obtain a first cutting result.

In the process of fusing the plurality of first sampling image blocks, each pixel can be fused according to the coordinate position of each first sampling image block in the corresponding first image, so that a fusion result with the same size as the first image is obtained. The fusion result includes the probability that each pixel point in the first image belongs to the organ region, and the predicted probability that each pixel point belongs to the organ region can be binarized based on a preset threshold, for example, each pixel point larger than the preset threshold can be marked as 1, and the region represented by the pixel point is the organ region; each pixel point less than or equal to the preset threshold may be marked as 0, and the area represented by the pixel point is a background area. Finally, each communication domain (namely, the communication domain formed by the pixel points marked as 1) in the fusion result can be filtered according to the volume of the communication domain, and the communication domain with the volume smaller than a specific threshold value is removed to obtain a first segmentation result, namely, the organ region in the first image.

By the method, the first image is cut in an overlapping mode, the obtained first sampling image blocks are input into the first segmentation network to obtain the segmentation results of the plurality of first sampling image blocks, and finally the segmentation results of the plurality of first sampling image blocks are fused to obtain the organ region in the first image, so that the information in the first image can be fully utilized, and the image segmentation accuracy is improved.

In one possible implementation, performing a second segmentation process on the first image to determine a lesion region in the first image includes: cutting the first image according to a second preset size to obtain a second sampling image block; inputting the second sampling image block into a second segmentation network for segmentation to obtain a segmentation result of the second sampling image block; and fusing the segmentation results of the plurality of second sampling image blocks to obtain a focus area in the first image.

It should be understood that the second segmentation process performed on the first image to determine the lesion region in the first image may refer to the process of determining the organ region performed on the first image as described above, and will not be described herein again. The second preset size may be the same as or different from the first preset size, and the disclosure is not limited thereto.

After the segmentation process is performed in step S11 to obtain the segmentation result, an abnormal subregion may be determined in step S12 based on the organ region and the lesion region in the segmentation result.

In one possible implementation, step S12 may include: acquiring a first space region which is externally connected with the focus region; expanding the first space region according to a preset expansion coefficient to obtain an expanded second space region; and determining a spatial region which belongs to the organ region and does not belong to the focus region in the second spatial region as the abnormal subregion.

For example, fig. 2 shows a schematic diagram of an anomaly region according to an embodiment of the present disclosure. As shown in fig. 2, a first spatial region circumscribed by the focal region (the aneurysm region shown in fig. 2), i.e., the region of the dashed box a1 shown in fig. 2, is obtained. Then, the geometric center of the circumscribed first spatial region is calculated, and the first spatial region is expanded according to a preset expansion coefficient with the geometric center as the center, so as to obtain an expanded second spatial region, that is, a dashed box a2 region shown in fig. 2.

As shown in fig. 2, in the second spatial region (a 2 region in fig. 2), a spatial region that belongs to the organ region (the blood vessel region in fig. 2) and does not belong to the lesion region (the aneurysm region in fig. 2) is determined as the abnormal sub-region, i.e., the gray region in fig. 2.

The first spatial region circumscribed to the lesion area may be a cuboid, a sphere, an ellipsoid, or other solid geometric figures, and the shape of the specific first spatial region is not limited by the present disclosure. The preset expansion coefficient is a positive real number greater than 1, and can be set according to the experience of a clinician, and the value of the specific expansion coefficient is not limited by the disclosure.

It should be understood that a focal region may include one or more connected regions, and each connected region may correspond to a focal. In the case that the lesion area includes a plurality of lesions, the image processing method of the present disclosure may process each connected region existing in the lesion area in parallel according to the method of step S12, to obtain an abnormal sub-area corresponding to each connected region.

By the method, the abnormal subarea is determined from the organ area according to the positions of the organ area and the focus area, so that the overlapping part of the abnormal subarea and the focus area can be eliminated, the calculation amount to be processed can be reduced, the calculation efficiency is improved, and the accuracy of a focus interface in the subsequent step can be improved.

After obtaining the abnormal sub-region in step S12, a lesion boundary may be determined according to the abnormal sub-region and the lesion region in step S13.

In one possible implementation, step S13 may include: determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point; and determining a focus interface from the plurality of reference planes according to the number of boundary reference points in the plurality of preset reference planes, wherein the plurality of reference planes are respectively vertical to all coordinate axes of the image coordinate system of the image to be processed.

For example, according to the distance between the first pixel point in the abnormal sub-region and the second pixel point in the focal region, each pixel point, from the first pixel point and the second pixel point, whose distance between the first pixel point and the second pixel point is less than or equal to the distance threshold is determined as the boundary reference point. Wherein the distance threshold may be set based on the experience of the clinician, and the disclosure is not limited.

After the dividing reference points are obtained, the number of the dividing reference points in each preset reference plane in a plurality of reference planes can be detected, the reference plane containing the largest number of the dividing reference points is determined, and a region formed by the dividing reference points in the reference plane is determined as a focus interface.

The preset reference planes are respectively vertical to all coordinate axes of an image coordinate system of the image to be processed. For example, assume that the image coordinate system of the image to be processed is an xyz rectangular coordinate system, and the coordinate axes are x-axis, y-axis, and z-axis. The reference plane may include planes perpendicular to the x-axis (i.e., parallel yoz plane), the y-axis (i.e., parallel xoz plane), and the z-axis (i.e., parallel xoy plane).

It should be understood that, in the actual clinical diagnosis, the doctor can determine the lesion boundary according to each two-dimensional slice image in the coronal, sagittal or axial position, and the reference plane preset in the present disclosure can represent each two-dimensional slice image in the coronal, sagittal or axial position.

By the method, the process of determining the focus interface by a clinician can be highly simulated, the accuracy of the focus interface is improved, and the accuracy of the morphological analysis result of the focus is improved in the subsequent steps.

In a possible implementation manner, determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point in step S13 includes:

determining a first distance between the first pixel point and each second pixel point in the focal region aiming at any first pixel point; under the condition that a first distance smaller than or equal to a distance threshold exists, determining the first pixel point as a boundary reference point;

determining a second distance between the second pixel point and each first pixel point in the abnormal subarea aiming at any second pixel point; and under the condition that a second distance smaller than or equal to the distance threshold exists, determining the second pixel point as a boundary reference point.

For example, assume that the anomalous sub-region includes N1 first pixel points P_iWherein i is 1, 2, …, N1, and N1 are positive integers; the focus area comprises N2 second pixel points Q_jWhere j is 1, 2, …, N2, N2 are positive integers, and the values of N2 and N1 may or may not be equal, and the disclosure is not limited thereto.

Aiming at any one first pixel point P_iDetermining a first pixel point P_iAnd each second pixel point Q_j(i.e., Q)₁，Q₂，…，Q_N2) First distance L therebetween_ij＝|P_i-Q_j|。

For example: first pixel point P_iAnd the second pixel point Q₁First distance L of_i1＝|P_i-Q₁|；

First pixel point P_iAnd the second pixel point Q₂First distance L of_i2＝|P_i-Q₂|；

By analogy, the first pixel point P_iAnd the second pixel point Q_N2First distance L of_iN2＝|P_i-Q_N2|；

At a first distance L_i1～L_iN2Under the condition that the first distance less than or equal to the distance threshold exists, the first pixel point P is connected with the first pixel point P_iDetermined as a demarcation reference point. Wherein the distance threshold may be set based on the experience of the clinician, and the disclosure is not limited.

Similarly, for any one of the second pixel points Q_jDetermining the second pixel point Q_jAnd each first pixel point P_i(i.e. P)₁，P₂，…，P_N1) A second distance L therebetween_ji＝|Q_j-P_i|。

For example: second pixel Q_jAnd the first pixel point P₁Second distance L_j1＝|Q_j-P₁|；

Second pixel Q_jAnd the first pixel point P₂Second distance L_j2＝|Q_j-P₂|；

By analogy, the second pixel point Q_jAnd the first pixel point P_N1Second distance L_jN1＝|Q_j-P_N1|；

At a second distance L_j1～L_jN1Under the condition that a second distance smaller than or equal to the distance threshold exists, a second pixel point Q is connected_jIs determined asA demarcation reference point.

It should be appreciated that in determining the first pixel point P_iAnd the second pixel point Q_jIn the Distance process, the Euclidean Distance (Euclidean Distance), Mahalanobis Distance (Mahalanobis Distance), Manhattan Distance (Manhattan Distance) or other Distance measurement modes between two pixels may be calculated, and the specific Distance measurement mode is not limited by the present disclosure.

By the method, the boundary reference point can be automatically determined according to the distance between the first pixel point and the second pixel point under the condition of no need of manual interaction. The method is simple, convenient and easy to realize, and is favorable for quickly determining the interface of the subsequent focus.

After the lesion boundary is obtained in step S13, a morphological analysis result of the lesion may be determined according to the lesion boundary and the lesion region in step S14.

In one possible implementation, the morphological analysis result of the lesion includes morphological parameters of the lesion, the morphological parameters include a reference diameter, a maximum diameter, a width, and a height, and step S14 may include:

determining the distance between two boundary reference points with the largest distance as the reference diameter of the focus in the boundary reference points included in the focus interface;

determining the maximum distance between a second pixel point and the geometric center of the focus interface as the maximum diameter of the focus in a second pixel point included in the focus area;

determining the distance between two second pixel points with the largest distance as the width of the focus in the direction vertical to the maximum diameter in the second pixel points included in the focus area;

and determining the maximum distance between the second pixel point and the focus interface as the height of the focus in a second pixel point included in the focus area.

For example, fig. 3 illustrates a schematic diagram of a morphological analysis result of a lesion according to an embodiment of the present disclosure, as shown in fig. 3, in which a distance between a demarcation reference point M1 and a demarcation reference point M2 is the largest among various demarcation reference points included in the lesion interface. The distance between demarcation reference point M1 and demarcation reference point M2 may be determined as the reference diameter of the lesion, i.e. the neck of the tumor as shown in fig. 3.

And the distance between the second pixel point M3 and the geometric center M0 of the focus interface is the largest in the second pixel points included in the focus area. The distance between the second pixel point M3 and the geometric center M0 of the lesion interface may be determined as the maximum diameter of the lesion.

Among the second pixel points included in the lesion region, in the direction perpendicular to the maximum diameter, the distance between the second pixel point M4 and the second pixel point M5 is the maximum, and the distance between the second pixel point M4 and the second pixel point M5 can be determined as the width of the lesion, that is, the tumor width shown in fig. 3.

Wherein, the direction of the maximum diameter is the direction of a straight line determined by the geometric center M0 of the second pixel point M3 and the focus interface.

Among the second pixel points included in the lesion region, the distance between the second pixel point M3 and the plane where the lesion interface is located is the largest, and the distance between the second pixel point M3 and the plane where the lesion interface is located is M6, so that the distance between the second pixel point M3 and the plane where the lesion interface is located can be determined as the height of the lesion, that is, the tumor height shown in fig. 3.

By the mode, the morphological analysis result of the focus is determined according to the focus interface and the focus area, so that the morphological analysis result of the focus can be automatically and accurately determined without the assistance of a doctor, the workload of the doctor is reduced, and the diagnosis and treatment efficiency of the doctor is improved.

Therefore, according to the image processing method disclosed by the embodiment of the disclosure, the organ region and the focus region can be determined by performing segmentation processing on the image to be processed, and the focus interface can be determined according to the position relationship between the organ region and the focus region; and finally, determining a morphological analysis result of the focus according to the focus interface and the focus area. The method can automatically and accurately determine the morphological analysis result of the focus in the image, thereby reducing the workload of medical staff and improving the working efficiency of the medical staff.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides an image processing apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the image processing methods provided by the present disclosure, and the descriptions and corresponding descriptions of the corresponding technical solutions and the corresponding descriptions in the methods section are omitted for brevity.

Fig. 4 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure, which includes, as shown in fig. 4:

a segmentation module 41, configured to perform segmentation on an image to be processed, and determine an organ region corresponding to a target organ in the image to be processed and a lesion region corresponding to a lesion on the target organ;

an abnormal sub-region determining module 42, configured to determine an abnormal sub-region from the organ region according to the positions of the organ region and the lesion region, where the abnormal sub-region corresponds to an organ portion bearing the lesion;

a lesion interface determining module 43, configured to determine a lesion interface between the abnormal sub-region and the lesion region according to a position relationship between a first pixel point in the abnormal sub-region and a second pixel point in the lesion region;

and a lesion analysis module 44, configured to determine a morphological analysis result of the lesion according to the lesion interface and the lesion region.

In one possible implementation, the segmentation module 41 includes: the pre-processing sub-module 411: the image processing device is used for carrying out normalization processing on an image to be processed to obtain a processed first image; the first segmentation submodule 412: the first segmentation processing is carried out on the first image, and an organ region in the first image is determined; second segmentation submodule 413: the second segmentation processing is carried out on the first image, and a lesion area in the first image is determined.

In one possible implementation, the first segmentation sub-module 412 is configured to: cutting the first image according to a first preset size to obtain a first sampling image block; inputting the first sampling image block into a first segmentation network for segmentation to obtain a segmentation result of the first sampling image block; fusing the segmentation results of the plurality of first sampling image blocks to obtain an organ area in the first image;

wherein the second partitioning submodule 413 is configured to: cutting the first image according to a second preset size to obtain a second sampling image block; inputting the second sampling image block into a second segmentation network for segmentation to obtain a segmentation result of the second sampling image block; and fusing the segmentation results of the plurality of second sampling image blocks to obtain a focus area in the first image.

In one possible implementation, the abnormal sub-region determination module 42 is configured to: acquiring a first space region which is externally connected with the focus region; expanding the first space region according to a preset expansion coefficient to obtain an expanded second space region; and determining a spatial region which belongs to the organ region and does not belong to the focus region in the second spatial region as the abnormal subregion.

In one possible implementation, the lesion interface determination module 43 is configured to: determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point; and determining a focus interface from the plurality of reference planes according to the number of boundary reference points in the plurality of preset reference planes, wherein the plurality of reference planes are respectively vertical to all coordinate axes of the image coordinate system of the image to be processed.

In one possible implementation, the morphological analysis of the lesion includes morphological parameters of the lesion including a reference diameter, a maximum diameter, a width, and a height, wherein the lesion analysis module 44 is configured to: determining the distance between two boundary reference points with the largest distance as the reference diameter of the focus in the boundary reference points included in the focus interface; determining the maximum distance between a second pixel point and the geometric center of the focus interface as the maximum diameter of the focus in a second pixel point included in the focus area; determining the distance between two second pixel points with the largest distance as the width of the focus in the direction vertical to the maximum diameter in the second pixel points included in the focus area; and determining the maximum distance between the second pixel point and the focus interface as the height of the focus in a second pixel point included in the focus area.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 5 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 5, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G), a third generation mobile communication technology (3G), a fourth generation mobile communication technology (4G) or a fifth generation mobile communication technology (5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 6 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as the Microsoft Server operating system (Windows Server), the graphical user interface based operating system (Mac OS XTM) available from apple Inc., the Multi-user Multi-Process computer operating system (Unix), the Unix-like operating system of free and open native code (LinuxTM), the Unix-like operating system of open native code (FreeBSDTM), or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An image processing method, characterized in that the method comprises:

performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a focus region corresponding to a focus on the target organ;

determining an abnormal subarea from the organ area according to the positions of the organ area and the focus area, wherein the abnormal subarea corresponds to an organ part bearing the focus;

determining a focus interface between the abnormal subarea and the focus area according to the position relation between a first pixel point in the abnormal subarea and a second pixel point in the focus area;

and determining a morphological analysis result of the focus according to the focus interface and the focus area.

2. The method of claim 1, wherein determining a lesion interface between the abnormal sub-region and the lesion region according to a positional relationship between a first pixel point in the abnormal sub-region and a second pixel point in the lesion region comprises:

determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point;

and determining a focus interface from the plurality of reference planes according to the number of boundary reference points in the plurality of preset reference planes, wherein the plurality of reference planes are respectively vertical to all coordinate axes of the image coordinate system of the image to be processed.

3. The method of claim 2, wherein determining a boundary reference point from the first pixel point and the second pixel point according to the distance between the first pixel point and the second pixel point comprises:

determining a first distance between the first pixel point and each second pixel point in the focal region aiming at any first pixel point;

under the condition that a first distance smaller than or equal to a distance threshold exists, determining the first pixel point as a boundary reference point;

determining a second distance between the second pixel point and each first pixel point in the abnormal subarea aiming at any second pixel point;

and under the condition that a second distance smaller than or equal to the distance threshold exists, determining the second pixel point as a boundary reference point.

4. The method of claim 1, wherein determining a deviant region from the organ region based on the locations of the organ region and the lesion region comprises:

acquiring a first space region which is externally connected with the focus region;

expanding the first space region according to a preset expansion coefficient to obtain an expanded second space region;

and determining a spatial region which belongs to the organ region and does not belong to the focus region in the second spatial region as the abnormal subregion.

5. The method of claim 1, wherein the morphological analysis of the lesion comprises morphological parameters of the lesion including a reference diameter, a maximum diameter, a width, and a height,

wherein the determining a morphological analysis result of the lesion according to the lesion interface and the lesion area comprises:

6. The method according to claim 1, wherein the segmentation processing of the image to be processed, and the determination of the organ region corresponding to the target organ in the image to be processed and the lesion region corresponding to the lesion on the target organ comprises:

normalizing the image to be processed to obtain a processed first image;

performing first segmentation processing on the first image to determine an organ region in the first image;

and performing second segmentation processing on the first image to determine a lesion area in the first image.

7. The method of claim 6, wherein performing a first segmentation process on the first image to determine an organ region in the first image comprises:

cutting the first image according to a first preset size to obtain a first sampling image block;

inputting the first sampling image block into a first segmentation network for segmentation to obtain a segmentation result of the first sampling image block;

fusing the segmentation results of the plurality of first sampling image blocks to obtain an organ area in the first image;

performing a second segmentation process on the first image to determine a lesion region in the first image, including:

cutting the first image according to a second preset size to obtain a second sampling image block;

inputting the second sampling image block into a second segmentation network for segmentation to obtain a segmentation result of the second sampling image block;

and fusing the segmentation results of the plurality of second sampling image blocks to obtain a focus area in the first image.

8. The method of any of claims 1-7, wherein the image to be processed comprises a three-dimensional angiographic image, the target organ comprises a blood vessel, the lesion on the target organ comprises an aneurysm, the deviant region comprises a parent artery region, and the lesion interface comprises a neck plane of the aneurysm.

9. An image processing apparatus characterized by comprising:

the segmentation module is used for performing segmentation processing on an image to be processed, and determining an organ region corresponding to a target organ in the image to be processed and a focus region corresponding to a focus on the target organ;

an abnormal sub-region determining module, configured to determine an abnormal sub-region from the organ region according to the positions of the organ region and the lesion region, where the abnormal sub-region corresponds to an organ portion bearing the lesion;

the focus interface determining module is used for determining a focus interface between the abnormal subregion and the focus region according to the position relationship between a first pixel point in the abnormal subregion and a second pixel point in the focus region;

and the focus analysis module is used for determining a morphological analysis result of the focus according to the focus interface and the focus area.

10. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 8.

11. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 8.