WO2020093563A1 - Procédé de traitement d'image médicale , système, dispositif et support d'informations - Google Patents

Procédé de traitement d'image médicale , système, dispositif et support d'informations Download PDF

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Publication number
WO2020093563A1
WO2020093563A1 PCT/CN2018/124660 CN2018124660W WO2020093563A1 WO 2020093563 A1 WO2020093563 A1 WO 2020093563A1 CN 2018124660 W CN2018124660 W CN 2018124660W WO 2020093563 A1 WO2020093563 A1 WO 2020093563A1
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image
lesion
medical image
abnormality
abnormal
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PCT/CN2018/124660
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English (en)
Chinese (zh)
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徐勇
罗笑玲
蒲祖辉
牟丽莎
胡吉英
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哈尔滨工业大学（深圳）
深圳市第二人民医院
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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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • 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
    • G06T2207/30041—Eye; Retina; Ophthalmic
    • 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
    • G06T2207/30096—Tumor; Lesion
    • 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 invention relates to the field of image processing, in particular to a medical image processing method, system, equipment and storage medium.
  • diabetes is considered to be the basis of many health problems and late-stage disorders, that is, diabetes can cause a series of diseases and complications, including: leading to serious heart disease, diabetic retinopathy (DR) and kidney problems.
  • DR diabetic retinopathy
  • DR detection technology is particularly important, and medical image analysis is one of the research fields that have attracted great interest from scientists and physicians.
  • DR can be divided into two major categories, namely non-proliferative diabetic retinopathy (NPDR) and proliferative retinopathy (PDR). There are three subcategories of NPDR: mild NPDR, moderate NPDR, and severe NPDR.
  • the non-proliferative type is the early stage of the lesion, and the lesion is limited to the retina, manifested as microhemangioma, hemorrhage, hard and soft exudate, retinal artery and venous lesions.
  • Proliferative lesions at least partially extend beyond the inner limiting membrane, and the appearance of neovascularization is a sign of proliferative.
  • DR lesions are described in detail as follows:
  • Microaneurysms represent the most primitive perceptible signs of retinal damage.
  • the abnormal permeability of retinal blood vessels leads to the formation of microaneurysms.
  • the microaneurysm can be regarded as a red dot with sharp edges and a size between 20 ⁇ m and 200 ⁇ m, which is approximately 8.25% of the size of the disc.
  • Rigid exudates are formed by the leakage of lipoproteins and other proteins from the blood vessels of the retina. Visually, it looks like small white or yellowish-white deposits with distinct edges. Rigid exudates are usually organized in the form of a ring, usually appearing in the outer layer of the retina. Rigid exudates are usually irregular and shiny, and are found close to the edges of microaneurysms or retinal edema.
  • RNFL retinal nerve fiber layer
  • Bleeding occurs due to leakage of weak blood vessels.
  • the focus of bleeding is in the form of red dots with different densities and uneven edges, and it is found in the range of 125 ⁇ m.
  • hemorrhage is divided into two categories: flame and dot blotting.
  • the first type originates from the anterior capillary artery and appears on nerve fibers.
  • the second type of dot blot bleeding is round and smaller than a microaneurysm. Dot blot bleeding can appear on the retina at different levels, however, in most cases it will appear at the end of the capillaries' meridians.
  • Neovascularization usually indicates the atypical appearance of new blood vessels that appear on the inner surface of the retina.
  • the new blood vessels are small and repeatedly penetrate into the vitreous cavity, which reduces visual ability and makes it significantly blurry, which eventually leads to blindness.
  • Macular edema is identified as a swollen part of the retina, which usually occurs due to the permeability of abnormal retinal capillaries. Macular edema causes leakage of fluid or other solutes around the macula and severely affects vision.
  • an object of the present invention is to provide a medical image processing method, system, device, and storage medium for improving the efficiency of processing and analyzing medical images.
  • the present invention provides a medical image processing method, including the following steps:
  • the lesion image acquisition step the lesion image is extracted based on the medical image
  • the lesion score is a first abnormal probability that the lesion image belongs to different image abnormalities
  • a second abnormal probability acquisition step acquiring multiple second abnormal probabilities of the medical image according to the medical image and the lesion image, the second abnormal probability being the probability that the medical image belongs to different image abnormalities;
  • the classification step according to a plurality of the first abnormal probability, a plurality of the second abnormal probability and different weighting coefficients, the final probability that the medical image belongs to different image abnormalities is obtained according to different image abnormalities, and the largest The degree of image abnormality of the final probability is taken as the degree of image abnormality of the medical image.
  • the medical image includes a fundus photograph.
  • the step of acquiring a lesion image includes:
  • step of generating scores includes:
  • the step of acquiring the second abnormal probability includes:
  • the degree of image abnormality includes normal, mild first abnormality, moderate first abnormality, severe first abnormality, and second abnormality.
  • the weight coefficient of the first abnormal probability is 0.2
  • the weight coefficient of the second abnormal probability is 0.8.
  • the present invention provides a medical image processing system, including:
  • the lesion image acquisition unit is used to extract the lesion image according to the medical image
  • a score generating unit configured to obtain a plurality of lesion scores based on the lesion image, where the lesion score is a first abnormal probability that the lesion image belongs to different image abnormalities;
  • a second abnormal probability obtaining unit configured to obtain a plurality of second abnormal probabilities of the medical image according to the medical image and the lesion image, the second abnormal probability being the probability that the medical image belongs to different image abnormalities ;
  • the classification unit is used to obtain the final probability that the medical image belongs to different image abnormalities according to different image abnormalities according to multiple first abnormal probabilities, multiple second abnormal probabilities and different weighting coefficients
  • the image abnormality of the final probability is taken as the image abnormality of the medical image.
  • the present invention provides a medical image processing device, including:
  • At least one processor and,
  • a memory communicatively connected to the at least one processor; wherein,
  • the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the medical image processing method.
  • the present invention provides a computer-readable storage medium that stores computer-executable instructions that are used to cause a computer to execute the medical image processing method.
  • the present invention obtains multiple first abnormal probabilities through the focus image, on the other hand obtains multiple second abnormal probabilities through the medical image and focus image, and then according to multiple first abnormal probabilities, multiple second abnormal probabilities and different
  • the weight coefficients are used to obtain the final probability of medical images belonging to different image abnormalities according to different image abnormalities, and the image abnormality of the maximum final probability is taken as the image abnormality of medical images to realize the analysis of medical image abnormality and overcome the existing
  • the present invention also obtains multiple lesion mask images of medical images by segmenting a neural network to further obtain multiple lesion images, ensuring accurate extraction of lesion images.
  • FIG. 1 is a flowchart of a specific embodiment of a medical image processing method in the present invention.
  • FIG. 2 is a schematic diagram of a specific embodiment of a segmented neural network of a medical image processing method in the present invention.
  • a medical image processing method includes the following steps:
  • the lesion image acquisition step the lesion image is extracted based on the medical image
  • the lesion score is the first abnormal probability that the lesion image belongs to different image abnormalities. For example, if the image abnormality includes both normal and abnormal, the score generation step is to obtain the focus image. Normal or abnormal probability, get two first abnormal probabilities, and so on;
  • the second abnormal probability obtaining step multiple second abnormal probabilities of the medical image are obtained according to the medical image and the lesion image, and the second abnormal probability is the probability that the medical image belongs to different image abnormalities;
  • the classification step is to obtain the final probabilities of medical images belonging to different image abnormalities according to different image abnormalities according to multiple first abnormal probabilities, multiple second abnormal probabilities and different weighting coefficients, and take the maximum final probability image abnormality as the The degree of abnormality of medical images. That is, the first abnormal probability and the second abnormal probability of the same image abnormal degree are added according to different weight coefficients to obtain the final probability of the image abnormal degree. Since there are multiple different image abnormal degrees, multiple final probabilities of the medical image can be obtained .
  • a medical image processing method On the one hand, multiple first abnormal probabilities are obtained from focus images, on the other hand, multiple second abnormal probabilities are obtained from medical images and focus images, and then multiple first abnormal probabilities and multiple second Abnormal probability and different weight coefficients are used to obtain the final probability that medical images belong to different image abnormalities according to different image abnormalities, and the image abnormality of the maximum final probability is taken as the image abnormality of medical images to realize the abnormality of medical images.
  • the analysis improves the processing and analysis efficiency of medical images, and overcomes the existing technical problems of low efficiency in relying on the naked eye for processing and analyzing pathological images.
  • medical images include, but are not limited to, fundus photos.
  • the fundus photos are used as examples to describe the medical image processing method.
  • the degree of image abnormality includes but is not limited to normal, mild first abnormality, moderate first abnormality, severe first abnormality, and second abnormality. Taking the fundus photo as an example, the first abnormality is a non-proliferative lesion. The second abnormality is a proliferative lesion.
  • FIG. 1 is a flowchart of a specific embodiment of a medical image processing method according to the present invention; the medical image processing method will be specifically described below:
  • Step 1 Pre-process the data.
  • the data used in the present invention is a two-dimensional color fundus photograph.
  • the original picture size is 3000 ⁇ 3000.
  • the picture needs to be pre-processed, and the background and other unnecessary parts of the picture are cropped to adjust the picture size to 128 ⁇ 128.
  • Step 2 The step of acquiring lesion images, including:
  • the main task of segmenting the neural network is to realize the segmentation and extraction of multiple lesions.
  • the types of lesions include microaneurysms, rigid exudates, and neonatal retinal blood vessels. Lesions, new retinal blood vessels are proliferative lesions.
  • the three masks are the micro-aneurysm mask, the hard exudate mask, and the retinal vascular mask.
  • the mask is specifically composed of 0 and 1, the background is 0, and the target is 1.
  • micro-aneurysm mask when generating a micro-aneurysm mask, predict whether each pixel belongs to the micro-aneurysm area by segmenting the neural network. If the pixel position is marked as 1, Otherwise, the pixel position is marked as 0, and finally a micro-aneurysm mask image with the same size as the original image of 128 ⁇ 128 is obtained.
  • the mask can accurately describe the location of the target. Corresponding the mask to the original image, you can extract the image of each lesion, which is the lesion image.
  • FIG. 2 is a schematic diagram of a specific embodiment of a segmentation neural network of a medical image processing method in the present invention
  • the implementation of the segmentation neural network is mainly based on a multi-task learning framework, and there are three segmentation tasks of the segmentation neural network , Respectively, segmentation of microaneurysm, segmentation of hard exudate, and segmentation of retinal vessels.
  • the first part of the network is called the "shared layer", and the weights of these layers are common to all three tasks.
  • the parameters between these layers are calculated independently and do not participate in cross-layer sharing. In these task-specific layers, the network learns task-specific information.
  • Each of these independent task layers will generate a separate output, specifically a segmentation mask that outputs microaneurysms, hard exudates, and retinal blood vessels.
  • the above segmentation neural network can segment multiple lesion images simultaneously, and is not limited to the three lesion images in this embodiment.
  • the network Before using the segmented neural network, the network needs to be trained. Specifically, the model is first initialized, and then the sample with the marked mask data is input for training. The entire picture is used for training without patch sampling. The experiment proves that it is more effective to use the whole picture.
  • N is the number of training samples
  • L MA is the pixel-level loss of micro-aneurysm segmentation
  • L HES is the segmentation loss of hard exudate
  • L NV is the segmentation loss of retinal vessels
  • f (x, ⁇ ) is the segmentation The segmentation result predicted by the neural network, where x is a pixel of the sample and ⁇ is the learning rate
  • S 1 , S 2 , and S 3 are the labeled positions of the samples of microaneurysm, hard exudate, and retinal blood vessel
  • ⁇ ( ⁇ ) Is a regular item.
  • the trained segmentation neural network uses the trained segmentation neural network to obtain a variety of lesion mask images of the fundus photos, including micro-aneurysm mask images, hard exudate mask images, and retinal vascular mask images; according to the lesion mask images and medicine
  • the image can be segmented to obtain a variety of lesion images, including micro-aneurysm lesion images, rigid exudate lesion images, and retinal vascular lesion images.
  • Step 3 The score generation step, including:
  • the features of multiple lesions are spliced and input into the first machine learning classification algorithm to obtain multiple lesion scores.
  • the features such as the color and shape of each lesion are extracted according to the lesion image as shown in Table 2, 86-dimensional features can be extracted from each lesion image, and the lesion features of the 3 lesion images are The sequence of micro-aneurysm lesion images, rigid exudate lesion images, and retinal vascular lesion images are stitched together into a 258-dimensional feature vector, and the feature vector is input into the score generator (that is, the first machine learning classification algorithm) to obtain multiple
  • the score of each lesion is i is the category of the abnormality of the image, X is the number of the medical image, that is, the acquired lesion image belongs to different image abnormality (including normal, mild non-proliferative lesions, moderate non-proliferative lesions, severe non-proliferative lesions and proliferative Disease), that is, the score of 5 lesions can be obtained.
  • the score generator uses a random forest algorithm.
  • the score generator needs to be trained before it is used. Specifically, the 258-dimensional feature vector of the lesion feature of the training sample and the degree of lesion marked by the sample are input into the score generator for training.
  • the trained score generator can calculate the probability that the input lesion belongs to 5 image abnormalities (that is, normal, mild non-proliferative lesions, moderate non-proliferative lesions, severe non-proliferative lesions, and proliferative lesions) , Take this probability value as the lesion score of the sample.
  • Step 4 The second abnormal probability acquisition step, including:
  • the image abnormality includes normal, mild non-proliferative lesions, moderate non-proliferative lesions, severe non-proliferative lesions, and proliferative lesions;
  • the second machine learning classification algorithm is designed based on 3D convolutional neural network
  • the 3D convolutional neural network can simultaneously input the original medical images (ie, fundus photos), microaneurysm lesion images, rigid exudate lesion images, and retinal vascular lesion images, which can extract features from multiple dimensions and then perform 3D convolution To capture feature information from multiple images.
  • the 3D convolutional neural network includes a solid-wired layer (ie, hard-connected layer), 4 convolutional layers, 3 down-sampled layers, and a fully connected layer.
  • the convolved cubes of each convolution kernel are 4 pictures including the original image, the image of the microhemangioma lesion, the image of the retinal vascular lesion, and the image of the hard exudate lesion.
  • the size of each picture is 128 ⁇ 128.
  • a fixed solid-line kernel is applied to process the input graph to generate multiple channels of information, then multiple channels are processed separately, and finally the information of all channels are combined to obtain the final Feature description.
  • This solid layer actually encodes a priori knowledge of the lesion, which is better than random initialization performance.
  • L 3 layer which is down-sampled with a 3 ⁇ 3 window in the feature map of the L 2 layer, so that the same number of lesion maps with reduced spatial resolution will be obtained.
  • the L 5 layer uses a 2 ⁇ 2 downsampling window, followed by two 2D convolutions and downsampling to obtain a 128 ⁇ 1 ⁇ 1 feature vector, and the 128 dimensions are determined based on past experience.
  • the input multiple images After inputting medical images, after multiple layers of convolution and downsampling, the input multiple images are converted into a 128-dimensional feature vector, which captures the feature information of the retinal fundus photo. Then the 128-dimensional feature vector obtained is input to the softmax layer.
  • the number of nodes in the softmax layer is consistent with the number of image abnormality categories, and each node is fully connected to the 128 nodes in L 9 .
  • the 3D convolutional neural network Before using the 3D convolutional neural network, you need to train it, first initialize the model, and then input the sample marked with abnormal degree for training. Input the original image of the sample, the image of the microhemangioma lesion, and the image of the hard exudate lesion during training. 4 pictures of retinal vascular lesion images for training.
  • the 3D convolutional neural network model is continuously optimized during the training process, and the parameters are adjusted until convergence. The goal of optimization is to continuously reduce the difference between the classification results predicted by the network and the abnormal degree of sample labeling, that is, to minimize the loss function L2:
  • N is the number of training samples
  • L softmax represents the network classification loss
  • f (x, ⁇ ) is the classification result predicted by the network, where x is a sample, ⁇ is the learning rate
  • C is the label category
  • ⁇ ( ⁇ ) is the regular term.
  • a medical image processing method a score fusion mechanism is proposed, and the lesion scores obtained from each lesion image are integrated into a classification network, which improves the sensitivity and classification accuracy of each lesion.
  • the score fusion classification framework of the present invention is not limited to the three lesions detected in the present invention, and can be expanded when more lesions are detected, and the system has strong adaptability.
  • a medical image processing system including:
  • the lesion image acquisition unit is used to extract the lesion image according to the medical image
  • the score generating unit is used to obtain multiple lesion scores according to the lesion image, and the lesion score is the first abnormal probability that the lesion image belongs to different image abnormalities;
  • a second abnormal probability acquisition unit configured to acquire multiple second abnormal probabilities of the medical image according to the medical image and the lesion image, the second abnormal probability is the probability that the medical image belongs to different image abnormalities;
  • the classification unit is used to obtain the final probabilities that medical images belong to different image abnormalities according to different image abnormalities according to multiple first abnormal probabilities, multiple second abnormal probabilities and different weight coefficients, and to abnormalize the images with the largest final probability
  • the degree is regarded as the abnormal degree of the medical image.
  • a medical image processing device including:
  • At least one processor and,
  • a memory communicatively connected to the at least one processor; wherein,
  • the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the medical image processing method.
  • the medical image processing method refer to the description in Embodiment 1, and no further description is required.
  • a computer-readable storage medium stores computer-executable instructions for causing a computer to execute the medical image processing method.
  • the medical image processing method refer to the description in Embodiment 1, and no further description is required.

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Abstract

La présente invention concerne un procédé de traitement d'image médicale, un système, un dispositif et un support d'informations. Selon un aspect, une pluralité de premières probabilités d'anomalie sont acquises au moyen d'une image de lésion et selon un autre aspect, une pluralité de secondes probabilités d'anomalie sont acquises au moyen d'une image médicale et de l'image de lésion. Ensuite, en fonction de la pluralité de premières probabilités d'anomalie, de la pluralité de secondes probabilités d'anomalie et de différents coefficients de pondération, et selon différents degrés d'anomalie d'image, une probabilité finale que l'image médicale appartienne aux différents degrés d'anomalie d'image est acquise et le degré d'anomalie d'image ayant la plus grande probabilité finale est défini comme étant le degré d'anomalie d'image de l'image médicale, ce qui permet d'obtenir une analyse du degré d'anomalie de l'image médicale et de surmonter les problèmes de l'état de la technique de confiance à l'œil nu et de faible efficacité dans le traitement et dans l'analyse d'images de pathologies.
PCT/CN2018/124660 2018-11-07 2018-12-28 Procédé de traitement d'image médicale , système, dispositif et support d'informations WO2020093563A1 (fr)

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