WO2021259390A2 - Procédé et appareil de détection de plaques calcifiées sur des artères coronaires - Google Patents

Procédé et appareil de détection de plaques calcifiées sur des artères coronaires Download PDF

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WO2021259390A2
WO2021259390A2 PCT/CN2021/122142 CN2021122142W WO2021259390A2 WO 2021259390 A2 WO2021259390 A2 WO 2021259390A2 CN 2021122142 W CN2021122142 W CN 2021122142W WO 2021259390 A2 WO2021259390 A2 WO 2021259390A2
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area
gray
organ
target
lesion
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Chinese (zh)
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WO2021259390A3 (fr
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梁隆恺
吴振洲
刘盼
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北京安德医智科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • the present disclosure relates to the technical field of computer vision, and in particular to a method and device for detecting coronary calcified plaques.
  • Cardiovascular disease is a disease that seriously endangers human health. Most cardiovascular diseases are caused by atherosclerotic plaques. Calcified plaque in the coronary arteries is a sign of the degree of atherosclerosis, which can predict the possibility of coronary artery disease in patients.
  • coronary angiography images can be obtained by injecting contrast agents.
  • contrast agents due to the thickness of blood vessels, the distribution of contrast dose in the blood vessels is different, resulting in different gray values of calcified plaques.
  • the present disclosure proposes a method and device for detecting coronary calcified plaque.
  • a lesion detection method includes: segmenting an image to be processed, and determining multiple target regions of a first organ in the image to be processed; for any target region, according to The gray information of the target area determines the first gray threshold and the second gray threshold of the target area, the first gray threshold is greater than the second gray threshold; according to the first gray threshold of the target area A gray-level threshold and a second gray-level threshold respectively determine the suspected lesion area and the pseudo-lesion area in the target area; determine the lesion of the first organ according to the suspected lesion area and the pseudo-lesion area of each of the target areas area.
  • performing segmentation processing on the image to be processed and determining multiple target regions of the first organ in the image to be processed includes: segmenting the image to be processed and determining that the image is The organ area of the organ area includes a second organ and a first organ, and the size of the second organ is larger than the size of the first organ; classifying the organ area to obtain multiple classification areas; The first area of each first organ and the second area of the second organ are determined from the plurality of classification areas; according to the first area and the second area, the majority of the image to be processed is determined Target areas.
  • determining the first area of each first organ and the second area of the second organ from the multiple classification areas includes: calculating the number of pixels in the multiple classification areas The most classified area is determined as the second area; among the plurality of classified areas, the classified areas other than the second area are determined as the first area; wherein, according to the first area, A region and the second region, determining multiple target regions in the image to be processed, including: performing an expansion operation on the first region to obtain an expanded third region; from the third region , Removing the area overlapping with the second area to obtain a cropped area; performing crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
  • determining the first gray threshold value and the second gray threshold value of the target area according to the gray information of the target area includes: determining the gray value of pixels in the target area Sort, determine the gray value of the median as the second gray threshold; add the second gray threshold and a preset offset threshold to obtain the first gray threshold.
  • respectively determining the suspected lesion area and the pseudo lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area includes: according to the target area The first gray-scale threshold value of the target area is determined as the suspected lesion area; the area where the pixel in the target area has a gray value greater than the first gray-scale threshold value is determined as the suspected lesion area; according to the second gray-scale threshold value of the target area , Determining the area where the pixel in the target area has a gray value greater than the second gray threshold value as a fourth area, the fourth area includes a plurality of connected areas; the number of pixels in each connected area is greater than The connected area with the preset threshold is determined as the pseudo-focus area.
  • determining the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas includes: removing and The area where the pseudo lesion area overlaps is the lesion area of the first organ.
  • the image to be processed includes a cardiac medical image
  • the first organ includes coronary blood vessels
  • the second organ includes aortic blood vessels
  • the target area includes a coronary blood vessel segment.
  • Area, the lesion area includes a calcified plaque area.
  • a lesion detection device which includes: a segmentation module for performing segmentation processing on an image to be processed, and determining multiple target regions of a first organ in the image to be processed; and a threshold module for For any target area, determine a first gray level threshold and a second gray level threshold of the target area according to gray level information of the target area, where the first gray level threshold is greater than the second gray level threshold;
  • the suspected lesion and pseudo-lesion determination module is used to determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold and the second gray threshold of the target area;
  • the lesion determining module is used for Determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the segmentation module includes: a segmentation sub-module: used to segment the image to be processed and determine the organ region in the image to be processed, the organ region including the second organ and the second organ An organ, the size of the second organ is greater than the size of the first organ; classification sub-module: used to classify the organ region to obtain multiple classification regions; the first region and the second region determination sub-module: It is used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas; the target area determination sub-module: is used to determine the first area and the second area according to the first area and the second area. Area, to determine multiple target areas in the image to be processed.
  • a segmentation sub-module used to segment the image to be processed and determine the organ region in the image to be processed, the organ region including the second organ and the second organ An organ, the size of the second organ is greater than the size of the first organ
  • classification sub-module used to classify the organ region to obtain multiple classification regions
  • the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, the classification area except for the second area is determined as the first area; wherein, the target area determination sub-module is used to: perform an expansion operation on the first area to obtain the expansion After the third area; from the third area, remove the area that overlaps the second area to obtain a cropped area; perform cropping processing on the image to be processed according to the cropped area to obtain the Describe multiple target areas.
  • the threshold determination module is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold;
  • the second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • the suspected lesion and pseudo-lesion determining module includes: a suspected lesion determining submodule, configured to determine the gray value of pixels in the target area greater than the first gray threshold value of the target area The area where the pixel of the first gray-scale threshold is located is determined as the suspected lesion area; the pseudo-lesion determination sub-module is configured to determine the gray-scale of the pixel in the target area according to the second gray-scale threshold of the target area.
  • the area where the pixel with the value greater than the second gray-scale threshold is determined to be the fourth area, and the fourth area includes a plurality of connected areas; the connected area with the number of pixels greater than the preset threshold in each connected area is determined as the Pseudo lesion area.
  • the lesion determination module is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each target area to obtain the lesion area of the first organ.
  • an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute The above method.
  • a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above method when executed by a processor.
  • multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area
  • the first gray-scale threshold and the second gray-scale threshold respectively determine the suspected lesion area and the pseudo-lesion area in each target area, and finally the suspected lesion area and the pseudo-lesion area in each target area are used to determine the lesion area of the image to be processed ( Such as calcified plaque area).
  • This method can identify calcified plaques by setting different thresholds at different positions of the first organ (such as coronary vessels) in the image to be processed. Omissions increase the recall rate of calcified plaques, and can significantly improve the accuracy of calcified plaque recognition.
  • Fig. 1 shows a flowchart of a lesion detection method according to an embodiment of the present disclosure.
  • Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure.
  • Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure.
  • Fig. 4 shows a schematic diagram of grayscale distribution of a target area according to an embodiment of the present disclosure.
  • Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure.
  • Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure.
  • Fig. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure.
  • FIG. 8 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • FIG. 9 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • Fig. 1 shows a flow chart of a focus detection method according to an embodiment of the present disclosure.
  • the focus detection method includes: in step S1, a segmentation process is performed on an image to be processed, and the image to be processed is determined Multiple target areas of an organ.
  • step S2 for any target area, the first gray threshold and the second gray threshold of the target area are determined according to the gray information of the target area, and the first gray threshold is greater than the first gray threshold. Two gray scale threshold.
  • step S3 the suspected lesion area and the pseudo lesion area in the target area are respectively determined according to the first gray threshold value and the second gray threshold value of the target area.
  • step S4 the lesion area of the first organ is determined according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the lesion detection method can be executed by electronic equipment such as a terminal device or a server, and the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, etc., other processing equipment It can be a server or a cloud server, etc.
  • the lesion detection method can be implemented by a processor invoking a computer-readable instruction stored in a memory.
  • the method can be executed by the server.
  • 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, for example, a computer tomography (Computed Tomography). , CT) images or MRI (Magnetic Resonance Imaging, MRI) images, etc.
  • the image to be processed may be a two-dimensional medical image or a three-dimensional medical image.
  • the present disclosure does not limit the type of image to be processed and the specific acquisition method.
  • the image to be processed includes an organ and a lesion on the organ.
  • the image to be processed is a cardiac medical image
  • the organ in the image to be processed may be a blood vessel in the heart, including the aorta.
  • Blood vessels and coronary vessels, lesions on organs can be calcified plaques on coronary vessels.
  • the present disclosure does not limit the specific organ types and the types of lesions on the organs. Among them, there may be one or more lesions on the organ, and the present disclosure does not limit the number of lesions on the organ.
  • the image to be processed may be segmented to determine multiple target regions of the first organ in the image to be processed.
  • the image to be processed includes, for example, a cardiac medical image
  • the first organ in the image to be processed includes, for example, coronary blood vessels
  • the target area in the image to be processed includes, for example, the area where the coronary blood vessel segments are located.
  • a segmentation network may be preset to perform segmentation processing on the image to be processed and determine multiple target regions of the first organ in the image to be processed.
  • the segmentation network can be a deep convolutional neural network, including multiple convolutional layers, multiple deconvolutional layers, fully connected layers, etc.
  • the specific segmentation networks that can be used include but are not limited to U-Network (U-NET) , V-Network (V-NET) and other network structures, the present disclosure does not limit the specific network structure of the segmented network.
  • the gradient descent method can be used to adjust the network parameters of the segmentation network to optimize the network parameters and improve the accuracy of the segmentation network.
  • the training data can be annotated with medical image data (for example, artificially annotated coronary vascular data) by professionals with medical background under the review of senior doctors, and establish a sample image database according to a preset ratio (for example, 9:1 ) Divide each sample image into a training set and a verification set.
  • a preset ratio for example, 9:1
  • the present disclosure does not limit the value of the preset ratio.
  • the first gray-scale threshold and the second gray-scale threshold of each target area can be determined according to the gray-scale information of each target area, and the first gray-scale threshold is greater than the second gray-scale threshold. Degree threshold.
  • the first gray threshold can be used to determine the suspected lesion area in the target area
  • the second gray threshold can be used to determine the pseudo lesion area in the target area. Since the gray information of each target area may be different, the first gray threshold and the second gray threshold of each target area may also be different.
  • each target area may be segmented by the first threshold according to the first gray threshold value of each target area to determine the suspected lesion area in each target area; according to each target area The second gray-scale threshold of the region, the second threshold segmentation is performed on each target region, and the pseudo-lesion region in each target region is determined.
  • step S4 according to the suspected lesion area and the pseudo-lesion area of each of the target areas, the area of the suspected lesion area that does not contain the pseudo-lesion area is determined as the lesion area of the first organ .
  • the lesion area includes, for example, a calcified plaque area of a coronary artery.
  • multiple target areas can be determined from the image to be processed, and for each target area, the first grayscale threshold and the second grayscale threshold of each target area can be determined respectively, and then according to the first grayscale threshold of each target area
  • the first gray-scale threshold and the second gray-scale threshold determine the lesion area of the image to be processed, and different thresholds can be set for different positions of the first organ (such as coronary blood vessels) in the image to be processed to identify calcified plaques. Not only can it effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
  • step S1 may include: step S11, segmenting the image to be processed, and determining an organ region in the image to be processed, the organ region including a second organ and a first organ , The size of the second organ is larger than the size of the first organ.
  • Step S12 Classify the organ regions to obtain multiple classification regions.
  • Step S13 Determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
  • Step S14 Determine multiple target areas in the image to be processed according to the first area and the second area.
  • Fig. 2 shows a schematic diagram of a segmentation process of a to-be-processed image according to an embodiment of the present disclosure.
  • the image 21 to be processed is a Computed Tomography Angiography (CTA) image
  • the image 21 to be processed is segmented to obtain a segmentation result 22, which determines the organ region in the image 21 to be processed, that is, the heart The area of blood vessels in an angiographic image.
  • the segmentation result 22 may be a binary label, marking the blood vessel area in the cardioangiography image as 1, and marking the background area outside the blood vessel area as 0.
  • the organ area is a blood vessel area, including the second organ and the first organ.
  • the second organ includes aortic vessels, that is, the organ area within the dashed frame in the segmentation result 22;
  • the first organ is a coronary vessel segment, that is, the organ area outside the dashed frame in the segmentation result 22.
  • the coronary vessels originate from the roots of the aortic vessels, and the size of the aortic vessels is larger than the size of the coronary vessels.
  • the coronary vessels are relatively small.
  • the same lesion may not affect the blood circulation in the aortic vessels, but may block the coronary vessels.
  • the brightness distribution of each branch blood vessel segment in the image to be processed is uneven. Therefore, after the organ area is obtained, it can be classified according to the size of each part on the organ.
  • step S12 the organ regions are classified to obtain multiple classification regions.
  • the organ region is a blood vessel region
  • the organ region is classified, that is, the blood vessel region is separated, and multiple blood vessel segments are obtained, and each blood vessel segment is a classification area.
  • the neural network algorithm can be used to input the organ region into a pre-trained classification network to obtain multiple classification regions; or, the organ region can be circumscribed by a rectangle. Cutting is performed to obtain a plurality of classification regions; among them, the plurality of classification regions obtained by the cutting can be respectively input into a preset first organ registration model for registration prediction, and the positional relationship between the classification regions is obtained.
  • step S13 may include: determining the classification area with the largest number of pixels in the plurality of classification areas as the second area; dividing the plurality of classification areas by The classification area outside the second area is determined to be the first area.
  • the organ area includes N classification areas, that is, classification area 1 to classification area N. Calculate the number of pixels in each of the N classification areas, determine the classification area with the largest number of pixels as the second area, and determine the remaining N-1 classification areas as the first area.
  • the organ area is the blood vessel area of the segmentation result 22 in FIG. Area
  • the first area is the branch blood vessel segment area outside the dashed box in the segmentation result 22 (ie, the coronary blood vessel area), including multiple branch blood vessel segment areas.
  • step S14 may include: performing an expansion operation on the first region to obtain a third region after expansion; from the third region, removing the region overlapping with the second region , Obtain a cropped area; perform crop processing on the image to be processed according to the cropped area to obtain the multiple target areas.
  • Fig. 3 shows a schematic diagram of a clipping region according to an embodiment of the present disclosure.
  • V other represents the first area (the solid rectangular area in Figure 3), which is the coronary vascular area
  • V main represents the second area (the cylinder area in Figure 3), which is the aortic vascular area .
  • the expansion operation is performed on the first area V other to obtain the expanded third area V other-big (the area in the dashed frame in FIG. 3 ).
  • the present disclosure does not limit the specific expansion operation method, and the morphological expansion operation can be performed on the first region V other ; and the first region V other can also be regionally expanded by a preset expansion coefficient.
  • first region V other in FIG. 3 uses one coronary artery segment as an illustration, and the first region V other is multiple coronary artery segment regions.
  • the shearing region V 1 is different from the second region V main. Intersection of multiple expanded coronary vascular segment regions.
  • the image to be processed is cropped to obtain multiple target areas.
  • a cropped image of the same size as the image to be processed can be determined according to the cropped area V 1.
  • the gray value of the pixels contained in the cropped area V 1 is marked as 1
  • the cropped area V The gray value of the pixels contained in the background outside 1 is marked as 0. Multiply the cropped image and the image to be processed to obtain the target area of the image to be processed.
  • the expansion operation on the first area in the organ area, it can be ensured that the calcified plaque part can be extracted together with the first area, and the recall rate and accuracy of the calcified plaque can be improved; by removing the area overlapping with the second area , It can reduce the amount of calculation and improve the efficiency of identifying calcified plaques.
  • multiple target regions of the first organ for example, coronary blood vessels
  • the first organ for example, coronary blood vessels
  • the first organ can be determined from the image to be processed, which is beneficial to subsequently setting appropriate thresholds according to the target regions of each part of the first organ, which can reduce the
  • the size of each part of the first organ is different, and the contrast dose is unevenly distributed on the first organ, which results in the influence of the different gray values of the calcified plaques on each part of the first organ, and it can also reduce the size of the first organ. Omission of calcified plaque at the site.
  • the gray level threshold of each target area can be determined according to the gray information of each target area.
  • step S2 may include: sorting the gray values of pixels in the target area, determining the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • FIG. 4 shows a schematic diagram of the grayscale distribution of a target area according to an embodiment of the present disclosure.
  • the pixels in any target area can be sorted according to the size of the gray value, and the abscissa in the figure represents the target area
  • the gray value of the middle pixel is A to B, and the vertical axis represents the number of pixels corresponding to the gray value.
  • the gray value range A to B of the pixel in the target area can be determined according to the image type of the image to be processed. For example, assuming that the image to be processed is a CT image, the gray value of the pixel in the target area (that is, the CT value ) The range is -1000HU ⁇ 1000HU, and HU is the Heinz unit of the CT value. The present disclosure does not limit the value range of the gray value.
  • the median gray value T 0 can be determined as the second gray threshold, and the second gray threshold T 0 is added to the preset offset threshold T 1 to obtain the first gray Degree threshold T fin .
  • the result of the addition of the second gray-scale threshold T 0 and the preset offset threshold T 1 needs to be less than the maximum gray-scale value of the pixel in the target area.
  • the preset offset threshold T 1 can be determined based on clinical experience. The specific value of the offset threshold T 1 is not limited.
  • first gray threshold T fin and second gray level can be determined according to the gray information of each target area.
  • Degree threshold T 0 can be determined according to the gray information of each target area.
  • different thresholds can be set for different positions of the first organ (for example, coronary blood vessels), reduce the influence of the inconsistency of brightness at different positions in the first organ, and improve the accuracy of calcified plaque recognition.
  • step S2 After the first gray-scale threshold and the second gray-scale threshold of each target area are determined in step S2, the suspected lesion area and the pseudo-lesion area of each target area can be determined through step S3.
  • step S3 may include: according to the first gray-scale threshold of the target area, determining the area where the pixel in the target area has a gray-scale value greater than the first gray-scale threshold, Determined as the suspected lesion area.
  • the second gray-scale threshold of the target area determine the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as a fourth area, and the fourth area includes a plurality of Connected areas: Connected areas where the number of pixels in each connected area is greater than a preset threshold are determined as the pseudo-focus area.
  • Fig. 5 shows a schematic diagram of a suspected lesion area and a pseudo-lesion area according to an embodiment of the present disclosure.
  • the cylindrical shape represents a target area.
  • the first gray threshold of the target area perform the first threshold segmentation process on the target area, and the gray value of the pixels in the target area is greater than the first gray scale.
  • the area where the threshold pixel is located is determined as the suspected lesion area 51 (the black solid line area in FIG. 5).
  • the fourth area 52 may include a plurality of connected areas, and a connected area with a pixel number greater than a preset threshold in each connected area is determined as a pseudo-focus area.
  • the fourth area in Figure 5 includes 3 connected domains, the number of pixels in the two gray dotted areas is less than the preset threshold, and the number of pixels in the black dotted area is greater than the preset threshold.
  • the black dotted area can be determined as Pseudo lesion area.
  • the preset threshold can be determined based on clinical experience.
  • the lesion is a calcified plaque on a coronary artery, which can be set to the maximum value of calcified plaque statistics based on clinical experience.
  • the present disclosure does not limit the specific value of the preset threshold. .
  • the process of determining the suspected lesion area and the pseudo-lesion area from one target area can be referred to in FIG. 5, and the suspected lesion area and the pseudo-lesion area of each target area can be determined respectively.
  • the suspected lesion area determined according to the second gray-scale threshold can increase the recall rate of calcified plaques
  • the pseudo-lesion area determined according to the first gray-scale threshold for example, the small branch blood vessels on the coronary vessels are high.
  • the bright part can reduce the false positive rate of calcified plaques. Therefore, by determining the suspected lesion area and the pseudo-lesion area, it is helpful to improve the accuracy of subsequent calcified plaque determination.
  • step S3 the suspected lesion area and the pseudo-lesion area of each target area are determined, and the lesion area of the first organ (for example, the calcified plaque area of coronary blood vessels) can be determined through step S4.
  • the lesion area of the first organ for example, the calcified plaque area of coronary blood vessels
  • step S4 may include: removing the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ (for example, coronary artery). Calcified plaque areas of blood vessels).
  • Fig. 6 shows a schematic diagram of a lesion area according to an embodiment of the present disclosure.
  • the cylindrical shape represents a target area
  • the solid line area represents the suspected lesion area 51
  • the dotted line area represents the pseudo lesion area 61 (including the gray area 51).
  • the area (gray area) overlapping with the pseudo lesion area 61 is removed to obtain the lesion area (black area).
  • determining the lesion area (such as the calcified plaque area) based on the suspected lesion area and the pseudo-lesion area is beneficial to increase the recall rate of calcified plaques, reduce the false positive rate of calcified plaques, and thus improve the recognition of calcified plaques Accuracy.
  • multiple target regions can be determined from the image to be processed, and for each target region, the first grayscale threshold and the second grayscale threshold of each target region can be determined respectively, and then according to The first gray-scale threshold and the second gray-scale threshold of each target area respectively determine the suspected lesion area and the pseudo-lesion area in each target area. Finally, the suspected lesion area and the pseudo-lesion area in each target area can be used to determine the target area. Process the image of the lesion area, such as the calcified plaque area.
  • the method is simple and easy to implement, not only can effectively reduce the omission of calcified plaques at the small branch vessels of the coronary blood vessels in the image to be processed, and improve the recall rate of calcified plaques, but also can significantly improve the accuracy of calcified plaque recognition.
  • the present disclosure also provides lesion detection devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the lesion detection methods provided in the present disclosure.
  • lesion detection devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the lesion detection methods provided in the present disclosure.
  • FIG. 7 shows a block diagram of a lesion detection device according to an embodiment of the present disclosure.
  • the device includes: a segmentation module 71, configured to perform segmentation processing on an image to be processed, and determine the first organ in the image to be processed Multiple target areas.
  • the threshold determination module 72 is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the second gray-scale threshold is configured to determine the first gray threshold and the second gray threshold of the target area according to the gray information of the target area for any target area, and the first gray threshold is greater than the gray level information.
  • the suspected lesion and pseudo-lesion determining module 73 is configured to respectively determine the suspected lesion area and the pseudo-lesion area in the target area according to the first gray threshold value and the second gray threshold value of the target area.
  • the lesion determination module 74 is configured to determine the lesion area of the first organ according to the suspected lesion area and the pseudo lesion area of each of the target areas.
  • the segmentation module 71 includes: a segmentation sub-module: used to segment the image to be processed and determine an organ region in the image to be processed, and the organ region includes a second organ and The size of the first organ, the second organ is larger than the size of the first organ.
  • Classification sub-module used to classify the organ regions to obtain multiple classification regions.
  • the first area and second area determining sub-module used to determine the first area of each first organ and the second area of the second organ from the multiple classification areas.
  • Target area determination sub-module used to determine multiple target areas in the image to be processed according to the first area and the second area.
  • the first region and the second region determining sub-module are configured to: determine the classification region with the largest number of pixels in the plurality of classification regions as the second region; Among the plurality of classification areas, classification areas other than the second area are determined as the first area.
  • the target area determining submodule is used to: perform an expansion operation on the first area to obtain a third area after expansion; from the third area, remove the area overlapping with the second area to obtain Cropping area; performing cropping processing on the image to be processed according to the cropping area to obtain the multiple target areas.
  • the threshold determination module 72 is used to: sort the gray values of pixels in the target area, determine the gray value of the median as the second gray threshold; The second gray-scale threshold is added to the preset offset threshold to obtain the first gray-scale threshold.
  • the suspected lesion and pseudo-lesion determining module 73 includes: a suspected lesion determining sub-module, configured to calculate the gray value of pixels in the target area according to the first gray level threshold of the target area The area where the pixel is larger than the first gray-scale threshold is determined as the suspected lesion area.
  • the pseudo-lesion determination sub-module is configured to determine, according to the second gray-scale threshold of the target area, the area where the pixel in the target area has a gray-scale value greater than the second gray-scale threshold as the fourth area,
  • the fourth area includes a plurality of connected areas; a connected area whose number of pixels in each connected area is greater than a preset threshold is determined as the pseudo-focus area.
  • the lesion determination module 74 is configured to remove the area overlapping with the pseudo-lesion area from the suspected lesion area of each of the target areas to obtain the lesion area of the first organ.
  • the image to be processed includes a cardiac medical image
  • the first organ includes coronary blood vessels
  • the second organ includes aortic blood vessels
  • the target area includes a coronary blood vessel segment.
  • Area, the lesion area includes a calcified plaque area.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
  • the electronic device can be provided as a terminal, server or other form of device.
  • FIG. 8 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the 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 in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the power supply component 806 provides power for various components of the electronic device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • 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 input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor can 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.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
  • the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the electronic device 800.
  • the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 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.
  • CMOS complementary metal oxide semiconductor
  • CCD charge coupled device
  • the sensor component 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 can access a wireless network based on communication standards, such as wireless network (WiFi), second-generation mobile communication technology (2G), third-generation mobile communication technology (3G), fourth-generation mobile communication technology (4G) or The fifth-generation mobile communication technology (5G), or a combination of them.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-available A programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to implement the above method.
  • FIG. 9 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server.
  • the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs.
  • the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-mentioned 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 the network, and an input output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on the operating system stored in the memory 1932, such as the Microsoft Server Operating System (Windows ServerTM), the graphical user interface operating system (Mac OS XTM) launched by Apple, and the multi-user and multi-process computer operating system (UnixTM) ), free and open source Unix-like operating system (LinuxTM), open source Unix-like operating system (FreeBSDTM) or similar.
  • Windows ServerTM Microsoft Server Operating System
  • Mac OS XTM graphical user interface operating system
  • UnixTM multi-user and multi-process computer operating system
  • LinuxTM free and open source Unix-like operating system
  • FreeBSDTM open source Unix-like operating system
  • a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling 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 instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded 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, optical fiber 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 the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user’s computer) connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by using the status information of the computer-readable program instructions.
  • FPGA field programmable gate array
  • PDA programmable logic array
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagram can represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
  • Executable instructions can be included in the blocks in the flowchart or block diagram.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software, or a combination thereof.
  • the computer program product is specifically embodied as a computer storage medium.
  • the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.
  • SDK software development kit

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Abstract

La présente divulgation concerne un procédé et un appareil de détection de plaques calcifiées sur des artères coronaires. Le procédé comprend les étapes consistant à : segmenter une image à traiter, et à déterminer une pluralité de régions cibles d'un premier organe dans ladite image ; pour n'importe quelle région cible, à déterminer une première valeur de seuil d'échelle de gris et une seconde valeur de seuil d'échelle de gris de la région cible selon des informations d'échelle de gris de la région cible ; à déterminer respectivement une région de lésion suspectée et une région de pseudo-lésion dans la région cible en fonction de la première valeur de seuil d'échelle de gris et de la seconde valeur de seuil d'échelle de gris de la région cible ; et à déterminer une région de lésion du premier organe en fonction des régions de lésion suspectée et des régions de pseudo-lésion des régions cibles. Selon les modes de réalisation de la présente divulgation, non seulement l'omission de plaques calcifiées au niveau de petites ramifications d'un vaisseau sanguin, à savoir d'une artère coronaire, dans une image à traiter peut être efficacement réduite, mais aussi le taux de rappel relatif aux plaques calcifiées est amélioré et la précision de l'identification de la présence de plaques calcifiées peut être considérablement améliorée.
PCT/CN2021/122142 2021-04-16 2021-09-30 Procédé et appareil de détection de plaques calcifiées sur des artères coronaires WO2021259390A2 (fr)

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