WO2023165033A1 - Procédé d'entraînement de modèle pour reconnaître une cible dans une image médicale, procédé de reconnaissance d'une cible dans une image médicale, et dispositif et support - Google Patents

Procédé d'entraînement de modèle pour reconnaître une cible dans une image médicale, procédé de reconnaissance d'une cible dans une image médicale, et dispositif et support Download PDF

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WO2023165033A1
WO2023165033A1 PCT/CN2022/095137 CN2022095137W WO2023165033A1 WO 2023165033 A1 WO2023165033 A1 WO 2023165033A1 CN 2022095137 W CN2022095137 W CN 2022095137W WO 2023165033 A1 WO2023165033 A1 WO 2023165033A1
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region
model
area
training
target
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Chinese (zh)
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潘晓春
王娟
陈素平
夏斌
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深圳硅基智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present disclosure relates to the field of image processing based on artificial intelligence, and in particular to a model training, method, device and medium for identifying targets in medical images.
  • the deep learning target recognition technology can obtain high recognition accuracy for large-sized targets, but the recognition performance for small targets (such as thin objects or small objects) is not satisfactory, which is likely to cause false positives and false alarms. , and it is also difficult to distinguish the categories of small objects.
  • small target signs such as spot hemorrhages and microvascular tumors are not easy to find and distinguish when deep learning is used for target recognition because the targets are small, light in color, and close in color. Therefore, how to effectively identify small targets remains to be studied.
  • the present disclosure is proposed in view of the above-mentioned state of the prior art, and its purpose is to provide a model training, method, device and medium for identifying targets in medical images that can effectively identify small targets.
  • the first aspect of the present disclosure provides a model training method for identifying a target in a medical image, including: acquiring the medical image as a training sample and a labeled region corresponding to the target in the training sample; determining the The region segmentation result corresponding to the marked region, and using the training sample and the region segmentation result to construct a training set, wherein the region segmentation result is obtained by under-segmenting the image data in the marked region; and based on The training set trains the model to be trained, and optimizes the model to be trained using a training loss function, wherein, in the training loss function, spatial weights are used to reduce the pair of pixels in the first region of the training sample to the Negative impact of the model to be trained, the first region is a region other than the target region of the target in the labeled region in the training sample, and the target region is determined by the region segmentation result.
  • obtaining the region segmentation result further includes: obtaining image data to be segmented based on the image data corresponding to the marked region in the training sample, Or obtain the image data to be segmented based on the image data corresponding to the marked region in the training sample and the image data corresponding to the marked region in the segmentation result of interest, wherein the segmentation result of interest is used to identify the A binary image of the region of interest of the training sample; and performing threshold segmentation on the image data to be segmented by using a target segmentation threshold, and then obtaining the region segmentation result, wherein the region segmentation result is a binary image.
  • the target region in the image data to be segmented can be identified through threshold segmentation, and when the labeled region includes a region other than the region of interest, noise outside the region of interest can be eliminated.
  • the target segmentation threshold is obtained according to the method for obtaining the threshold value of the label category to which the target belongs, wherein the method for obtaining the threshold value of each label category is determined by The average area and average color of each label category are determined, and the threshold value acquisition method includes the first method and the second method, and the average area of the label category corresponding to the first method is larger than the label corresponding to the second method The average area of the category and the average color of the label category corresponding to the first method is lighter than the average color of the label category corresponding to the second method; for the first method, find the threshold, so that the to-be-segmented The area of the pixel whose grayscale value is greater than the threshold in the image data is less than a preset multiple of the area of the image data to be segmented, and the threshold is used as the target segmentation threshold, wherein the preset multiple is greater than 0 and less than 1.
  • the target segmentation threshold can be obtained according to the characteristics of the label category corresponding to the target. Accordingly, the accuracy of threshold segmentation can be improved.
  • an erosion operation is also performed on the threshold segmentation result of the image data to be segmented to obtain at least one connected region, selecting the connected region whose center is closest to the center of the image data to be segmented from the at least one connected region as the region segmentation result.
  • the pixels in the first region in the training sample are assigned a first weight, wherein the The first weight is 0.
  • samples of undetermined categories can be ignored to reduce the negative impact of samples of undetermined categories on the model to be trained.
  • the pixels in the first region, the second region, the third region and the fourth region in the training sample are respectively assigned the first A weight, a second weight, a third weight, and a fourth weight, wherein the second area is the target area, the third area is an area in the area of interest that does not belong to the marked area, and the The fourth area is an area outside the area of interest, the first weight is less than the second weight and less than the third weight, the fourth weight is less than the second weight and less than the third weight Weights.
  • the negative impact of pixels of undetermined categories and pixels outside the region of interest on the model to be trained can be suppressed, and the positive influence of the target region within the target region and the non-target region within the region of interest on the model to be trained can be improved.
  • the accuracy of the model can be improved.
  • the model to be trained is a semantic segmentation model
  • a prediction result of the model to be trained is a semantic segmentation result of the training sample.
  • the shape of the labeled region is a rectangle.
  • the difficulty of labeling can be reduced.
  • a second aspect of the present disclosure provides an electronic device, which includes: at least one processing circuit configured to execute the steps of the model training method described in the first aspect of the present disclosure.
  • a third aspect of the present disclosure provides a computer-readable storage medium, the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the steps of the above-mentioned model training method are implemented.
  • a fourth aspect of the present disclosure provides a method for identifying a target in a medical image, the method comprising: acquiring the medical image as an input image; and using at least one model training method according to the first aspect of the present disclosure to train training a model, determining a prediction result of each trained model for the input image, and obtaining a target prediction result based on the prediction result of the at least one trained model.
  • the prediction results of each trained model include the probability that each pixel in the input image belongs to the corresponding label category, and the Integrating the prediction results of at least one trained model to obtain an integrated probability that each pixel of the input image belongs to the corresponding label category, determining a connected region based on the integrated probability, and obtaining the target prediction corresponding to each label category based on the connected region
  • the probability is used as the integrated probability
  • the average value of the prediction results of multiple trained models is calculated to obtain the probability that each pixel in the input image belongs to the corresponding label category
  • the probability mean is used as the integrated probability. In this case, obtaining the target prediction result based on the integrated probability can further improve the accuracy of the target prediction result.
  • the medical image is a fundus image.
  • the trained model is able to recognize small objects in fundus images.
  • the target includes microangioma, spotting, sheet bleeding and linear bleeding.
  • the trained model is able to recognize small objects in fundus images.
  • a fifth aspect of the present disclosure provides an electronic device, the electronic device comprising: at least one processing circuit configured to: acquire the medical image as an input image; and utilize the medical image according to the first aspect of the present disclosure
  • the at least one trained model trained by the model training method determines a prediction result of each trained model for the input image, and obtains a target prediction result based on the prediction result of the at least one trained model.
  • a model training, method, device and medium for identifying targets in medical images capable of effectively identifying small targets are provided.
  • FIG. 1 is a schematic diagram showing an example of a recognition target environment related to an example of the present disclosure.
  • FIG. 2 is a flowchart illustrating an example of a model training method related to an example of the present disclosure.
  • FIG. 3 is a schematic diagram illustrating some examples of labeled regions related to examples of the present disclosure.
  • FIG. 4 is a schematic diagram showing some example region segmentation results related to examples of the present disclosure.
  • FIG. 5 is a flowchart illustrating an example of acquiring a result of area division related to an example of the present disclosure.
  • FIG. 6 is an architecture diagram showing an example of a model to be trained using the U-Net architecture involved in an example of the present disclosure.
  • FIG. 7 is a schematic diagram illustrating several areas of some examples to which examples of the present disclosure relate.
  • FIG. 8 is a flowchart illustrating an example of a method of recognizing an object in an image according to an example of the present disclosure.
  • circuitry herein may refer to hardware circuits and/or a combination of hardware circuits and software.
  • model in this disclosure is capable of processing an input and providing a corresponding output.
  • neural network deep neural network
  • model network
  • neural network model the terms “neural network”, “deep neural network”, “model”, “network” and “neural network model” are used interchangeably.
  • this paper mentions the rectangular characteristics (such as side, width, height, width, and height) of related objects (such as labeled regions, image data to be segmented, and objects). If the object itself is not rectangular, unless otherwise specified, it can be defaulted Rectangle property that is the bounding rectangle of the object.
  • the present disclosure obtains a region segmentation result by under-segmenting the labeled region, and uses the result as a gold standard to segment the image, thereby realizing accurate recognition of small objects.
  • the present disclosure adopts a spatial weight method to deal with the negative impact of pixels of undetermined categories on image segmentation caused by under-segmentation in the labeled region. In this case, small targets can be effectively identified.
  • examples of the present disclosure propose a scheme for training models and recognizing objects in images to address one or more of the above-mentioned problems and/or other potential problems.
  • the scheme adopts the method of image segmentation for target recognition (that is, image segmentation is first performed on the image data in the marked area in the training sample to obtain the area segmentation result, and then the area segmentation result is post-processed to obtain the target recognition result).
  • this scheme identifies pixels of undetermined categories in the labeled region by under-segmenting the image data in the labeled region in the training sample, and combines spatial weights (that is, the setting of the weights can be related to the position of the pixels) to neural networks.
  • the network model is trained to reduce the negative impact of pixels of undetermined categories in the labeled region on the neural network model, which can improve the accuracy of the prediction result of the trained model on the input image (eg, medical image).
  • the trained model can be a trained neural network model (that is, a trained neural network model.
  • a trained neural network model For example, a trained semantic segmentation model.
  • the trained model may be an optimal neural network model obtained after training.
  • Examples of the present disclosure relate to training models and schemes for recognizing objects in images, which efficiently recognize small objects.
  • the model training method for recognizing objects in images involved in the examples of the present disclosure may be simply referred to as a model training method or a training method. It should be noted that the solutions involved in the examples of the present disclosure are also applicable to the recognition of large objects.
  • Examples of the present disclosure may refer to images from cameras, CT scans, PET-CT scans, SPECT scans, MRI, ultrasound, X-rays, angiograms, fluoroscopy, capsule endoscopic images, or combinations thereof.
  • the images may be medical images.
  • medical images may include, but are not limited to, fundus images, lung images, stomach images, chest images, brain images, and the like.
  • small targets in medical images can be identified.
  • the image can be a natural image.
  • the natural image may be an image observed or captured in a natural scene.
  • small objects in natural images can be identified.
  • other types of images can be used without limitation.
  • FIG. 1 is a schematic diagram showing an example of a recognition target environment 100 related to an example of the present disclosure.
  • recognition target environment 100 may include computing device 110 .
  • Computing device 110 may be any device having computing capabilities.
  • computing device 110 may be a cloud server, a personal computer, a mainframe, a distributed computing system, and the like.
  • the computing device 110 can obtain an input 120 and generate an output 140 corresponding to the input 120 by using a neural network model 130 (sometimes also referred to as a model to be trained 130 or a model 130 for short).
  • the input 120 may be the above-mentioned image
  • the output 140 may be a prediction result, training parameters (eg, weights), or performance indicators (eg, accuracy rate and error rate), etc.
  • the neural network model 130 may include, but is not limited to, a semantic segmentation model (eg, U-Net), or other models related to image processing.
  • the neural network model 130 may be implemented using any suitable network structure. For example, convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN), etc.
  • CNN convolutional neural network
  • RNN recurrent neural network
  • DNN deep neural network
  • the recognition target environment 100 may further include a model training device and a model application device (not shown).
  • the model training device can be used to implement the training method of training the neural network model 130 to obtain a trained model.
  • the model application device can be used to implement a related method for obtaining prediction results using a trained model to identify objects in an image.
  • the neural network model 130 may be the model 130 to be trained.
  • the neural network model 130 may be a trained model.
  • FIG. 2 is a flowchart illustrating an example of a model training method related to an example of the present disclosure.
  • the model training method can be performed by the computing device 110 shown in FIG. 1 .
  • the model training method can train a model that recognizes objects in medical images.
  • the model training method may include step S102.
  • step S102 medical images serving as training samples and labeled regions corresponding to targets in the training samples may be acquired. That is, in the training phase, medical images can be obtained as training samples. Thereby, it is possible to recognize the target in the medical image.
  • the medical images may be color images. Thus, the accuracy of recognizing small targets can be improved.
  • the medical image may contain a corresponding target, and the target may belong to at least one category of interest (that is, the category that needs to be identified).
  • the target may include small targets such as microangioma, spot hemorrhage, sheet hemorrhage, and linear hemorrhage.
  • the trained model is able to recognize small objects in fundus images.
  • FIG. 3 is a schematic diagram illustrating some examples of labeled regions related to examples of the present disclosure.
  • objects in the training samples may be labeled to obtain labeled regions.
  • the shape of the labeled region can be a rectangle, a circle, or a shape that matches the shape of the object in the training sample (for example, the shape of the labeled region can be the outline of the object).
  • the shape of the marked area can be a rectangle.
  • FIG. 3 shows a marked area D1 in a fundus image, where the marked area D1 is rectangular in shape, and the target in the marked area D1 is a sheet hemorrhage.
  • the marked region may have a corresponding marked label (that is, marked category of the target), and the marked label may be used to distinguish the category of the target.
  • Label categories can be in one-to-one correspondence with target categories.
  • the target category and label category may respectively include but not limited to microvascular tumor, spot hemorrhage, sheet hemorrhage, linear hemorrhage, and the like.
  • corresponding label categories may be represented numerically.
  • the labeled regions and corresponding labeled labels may be referred to as labeled results.
  • the model training method may further include step S104.
  • step S104 a region segmentation result (also called a pseudo-segmentation result) corresponding to the labeled region in the training sample may be determined, and a training set may be constructed using the training sample and the region segmentation result. It should be noted that, in some other examples, it is not necessary to determine the region segmentation result corresponding to the marked region, as long as the target region in the marked region (described later) can be identified, and the pixels in the target region are determined to belong to the target. Can.
  • the training samples can be preprocessed and then used to construct the training set. set.
  • preprocessing the training samples may include unifying the size of the training samples.
  • the size of the training samples can be unified to 1024 ⁇ 1024 or 2048 ⁇ 2048.
  • the disclosure does not limit the size of the training samples.
  • preprocessing the training samples may include clipping the training samples.
  • a region of interest in the training sample may be obtained and the region of interest may be used to clip the training sample. Accordingly, it is possible to make the size of the training samples uniform and include the region of interest.
  • the region of interest may be a region where objects may exist (also referred to as a foreground region).
  • the region of interest may be a fundus region.
  • training samples may be segmented to obtain regions of interest.
  • threshold segmentation may be performed on the training samples to obtain segmentation results of interest, wherein the segmentation results of interest may be used to identify regions of interest of the training samples. Thereby, a region of interest can be identified.
  • the segmentation result of interest obtained through threshold segmentation may be a binary image (also referred to as a binary image). It can be understood that, although the segmentation result of interest is obtained through threshold segmentation above, other segmentation results suitable for obtaining the segmentation result of interest are also applicable. For example, the segmentation result of interest can be obtained through a neural network.
  • performing threshold segmentation on the training sample may be to divide the training sample into a preset number of shares (for example, 9 equal parts), determine the segmentation threshold based on the gray values of the four corners of the training sample and the central area, and based on The segmentation threshold performs threshold segmentation on the training samples, and then obtains the segmentation results of interest.
  • the determination of the segmentation threshold based on the gray values of the four corner areas and the central area of the training sample may be the gray mean value of the pixels in each area in the four corner areas and the gray level of the pixels in the middle area The average value of the mean is used as the segmentation threshold for threshold segmentation, and then the segmentation result of interest is obtained.
  • an erosion operation may be performed on the segmentation result of the threshold corresponding to the training sample (that is, the initial segmentation result) to obtain the segmentation result of interest.
  • two erosion operations may be performed on the threshold segmentation results of the training samples to obtain the segmentation results of interest, where the size of the erosion kernel may be 5. In this way, noise at the edge of the region of interest (for example, the fundus region) can be eliminated.
  • the region segmentation result corresponding to the labeled region may be determined.
  • the region segmentation result can be used to determine the object region of the object within the labeled region.
  • the target area in the labeled area can be identified, and then pixels of undetermined categories can be determined based on the target area.
  • the pixels outside the target area in the marked area in the training sample may be pixels of undetermined categories.
  • the region segmentation result may be any form of data (for example, an image) that can identify the target region.
  • the region segmentation result may be a binary image.
  • the region corresponding to the pixel with a value of 1 can be set as the target region (that is, if the value of the pixel is 1, it can represent the pixel at the corresponding position in the training sample Belonging to the target, if the value of the pixel is 0, it can indicate that the pixel at the corresponding position in the training sample is a pixel of an undetermined category). In this case, it is possible to reduce the negative impact of pixels of undetermined categories on the model to be trained 130 .
  • FIG. 4 is a schematic diagram showing some example region segmentation results related to examples of the present disclosure.
  • FIG. 4 shows the region segmentation result A1 corresponding to the labeled region D1 in FIG. 3 , where D2 is the target region.
  • the region segmentation result A1 in FIG. 4 is the result of an equal scale enlargement, which does not represent a limitation to the present disclosure.
  • the region segmentation result A1 in FIG. 4 can actually be compared with the labeled region D1 of the same size.
  • under-segmentation may be performed on the image data in the marked region in the training sample to obtain the region segmentation result (that is, the target region corresponding to the target in the marked region may be segmented through under-segmentation to obtain the region segmentation result).
  • region segmentation result that is, the target region corresponding to the target in the marked region may be segmented through under-segmentation to obtain the region segmentation result.
  • pixels of undetermined categories in the labeled region can be identified based on the region segmentation result obtained by under-segmentation.
  • the mis-segmentation of the foreground object as the background but the background is not mis-segmented as the foreground object can be called under-segmentation.
  • under-segmentation can be that the pixels belonging to the target in the labeled area are mis-segmented as non-targets but the pixels in the labeled area that do not belong to the target are not mis-segmented as targets.
  • the pixels in the target area in the area segmentation result can be determined to belong to the target.
  • pixels outside the target region do not necessarily belong to the target (that is, they may be pixels of undetermined categories).
  • the region segmentation results corresponding to the marked regions may be determined based on the training samples and the image data respectively corresponding to the marked regions in the above segmentation results of interest.
  • the image data corresponding to the labeled region in the training sample (hereinafter referred to as the first image data corresponding to the labeled region in the training sample) and the above-mentioned segmentation result of interest (that is, the segmentation result of interest can be
  • the image data corresponding to the marked region in the binary image used to identify the region of interest of the training sample (hereinafter referred to as the second image data corresponding to the marked region in the segmentation result of interest) is subjected to a product operation to obtain the image to be segmented data (that is, the image data in the marked area), the image data to be segmented is under-segmented to determine the area segmentation result corresponding to the marked area.
  • the labeled region includes regions other than the region of interest, noise outside the region of interest can be eliminated.
  • FIG. 5 is a flowchart illustrating an example of acquiring a result of area division related to an example of the present disclosure. That is, some examples of the present disclosure obtain the flow of region segmentation results.
  • obtaining the region segmentation result may include step S202.
  • image data to be segmented may be acquired based on the labeled region.
  • the first image data may be the image data corresponding to the labeled region in the training sample
  • the second image data may be the image data corresponding to the labeled region in the segmentation result of interest. That is, the first image data and/or the second image data may be acquired based on the marked region, and then the image data to be segmented may be acquired based on the first image data, or the first image data and the second image data.
  • image data to be segmented may be obtained based on the first image data.
  • the image data to be segmented may be acquired based on color channels (eg, red channel, green channel, blue channel) of the first image data. Taking the fundus image as an example, the image data to be segmented may be acquired based on the green channel of the first image data.
  • the first image data corresponding to the labeled region can be obtained (for example, cropped) from the training sample, and then the green channel (ie, G channel) of the first image data is taken, and the image to be segmented is obtained based on the green channel of the first image data data.
  • the corresponding color channel (green channel) of the first image data may be used as the image data to be segmented to obtain the image data to be segmented.
  • the color space and color channel can be selected according to the characteristics of the medical image itself, which is not particularly limited in this disclosure.
  • the image data to be segmented may be acquired based on the first image data and the second image data. In this case, when the labeled region includes regions other than the region of interest, noise outside the region of interest can be eliminated.
  • the first image data, the second image data, and the image data to be segmented may represent image data (for example, pixel data, data stream or image) of the corresponding area, and in practice, the pixels of the corresponding area may be The value of or the position mark of the pixel is stored in a corresponding medium (such as a memory or a disk) to form a corresponding form of image data, which can be conveniently processed.
  • a corresponding medium such as a memory or a disk
  • the shapes of the first image data, the second image data, and the image data to be segmented can match the shape of the marked area, or can be a circumscribed rectangle of the marked area, which can be selected according to the method of obtaining the area segmentation result.
  • the process of obtaining the region segmentation result if it is necessary to use the rectangular characteristics of the image data to be segmented (for example, side, length, width, height, four corners) and the shape of the marked region is not rectangular, it can be based on the circumscribed region of the marked region
  • the area corresponding to the rectangle acquires the image data to be segmented. That is, after the shape of the labeled region is converted into a rectangle, the image data to be segmented can be obtained based on the converted labeled region.
  • obtaining the region segmentation result may further include step S204.
  • step S204 threshold segmentation may be performed on the image data to be segmented to obtain a region segmentation result.
  • the examples of the present disclosure are not limited thereto.
  • the image data to be segmented may be under-segmented in other ways to obtain the region segmentation result.
  • a target segmentation threshold (described later) may be used to perform threshold segmentation on the image data to be segmented, and then obtain a region segmentation result.
  • the target region in the image data to be segmented can be identified through threshold segmentation.
  • the value of the pixel whose gray value is not less than the target segmentation threshold in the image data to be segmented can be set to 1, and the value of other pixels to be 0, and then the region segmentation result can be obtained.
  • an erosion operation may also be performed on the threshold segmentation result (that is, the initial segmentation result) of the image data to be segmented. In this case, it is possible to reduce the probability of isolated pixels in the threshold segmentation result due to the influence of noise.
  • the erosion kernel k may satisfy the formula:
  • h can represent the height of the marked area (that is, the marked area corresponding to the image data to be segmented) and w can represent the width of the marked area, H can represent the height of the training sample, W can represent the width of the training sample, and p can represent preset hyperparameters.
  • a corrosion kernel of an appropriate size can be obtained according to the size of the training sample, the size of the labeled region, and the preset hyperparameters. Thereby, excessive corrosion can be suppressed.
  • preset hyperparameters may be used to tune the size of the corrosion kernel.
  • smaller corrosion nuclei can be used for particularly small objects. Thereby, it is possible to avoid an excessive erosion operation which would cause the target area of a particularly small target to disappear.
  • the preset hyperparameters may be fixed values. In some examples, the preset hyperparameters may be determined according to the average size of objects of the same category in the medical images. In some examples, the preset hyperparameters may be determined according to the average width and average height of objects of the same category in the medical images. In some examples, the preset hyperparameter p can satisfy the formula:
  • the medical image may be an image in a data source for obtaining preset hyperparameters.
  • the width and height of objects of the same category in multiple training samples and the width and height of the training samples may be counted to obtain relevant parameters of preset hyperparameters. That is, the data source can be training data.
  • the width and height of the target when acquiring preset hyperparameters in a medical image with labeled regions (for example, a training sample), may also be the width and height of the corresponding labeled region. Thus, the width and height of the target can be acquired conveniently.
  • the threshold segmentation result of the image data to be segmented there may be multiple connected regions in the threshold segmentation result of the image data to be segmented.
  • an erosion operation may be performed on the threshold segmentation result of the image data to be segmented to obtain at least one connected region, and a connected region whose center is closest to the center of the image data to be segmented is selected from the at least one connected region as the region segmentation result.
  • the connected region closest to the center of the image data to be segmented may represent the identified target region.
  • contours can be searched for corrosion results (that is, at least one connected region), and the preset number (for example, 3) contours with the largest area are taken as candidates, and the distance between the center of the contour and the image data to be segmented is retained in the candidate contours.
  • the connected region corresponding to the nearest contour of the center of is the region segmentation result.
  • the object segmentation threshold can be obtained according to the common big law (OTSU) way.
  • OTD common big law
  • at least one manner of acquiring the object segmentation threshold may be selected from the manners described in the examples of the present disclosure.
  • the target segmentation threshold may be obtained according to the label category to which the target belongs.
  • the object segmentation threshold may be acquired according to an acquisition threshold method of the label category to which the object belongs.
  • the target segmentation threshold can be obtained according to the characteristics of the label category corresponding to the target. Accordingly, the accuracy of threshold segmentation can be improved.
  • the method for obtaining the threshold value of the labeled category may include the first method and the second method.
  • the labeled categories of the objects in the training samples can be known.
  • the labeling category to which the target in the training sample belongs may be the labeling label in the labeling result.
  • the method for obtaining the threshold value of each labeling category may be obtained through the features of each labeling category.
  • the acquisition threshold method may be determined according to the average area and average color of each label category.
  • the examples of the present disclosure are not limited thereto, and in some other examples, the method for obtaining the threshold value of the label category may also be determined empirically.
  • the first method can be used for sheet hemorrhage in the fundus image
  • the second method can be used for microvascular tumors, spot hemorrhages, and linear hemorrhages in the fundus image.
  • the average area and average color of each label category may be fixed values, which may be obtained according to statistics on sample data.
  • the area and color of objects of the same category eg, for the training sample, the same category may refer to the same labeled category
  • sample data eg, training samples
  • the fixed value may also be an empirical value.
  • the average area of the label category corresponding to the first method may be greater than the average area of the label category corresponding to the second method and The average color of the label category corresponding to the first method may be lighter than the average color of the label category corresponding to the second method.
  • the first method can be used for objects with large areas and light colors (for example, flake hemorrhages in fundus images).
  • the second method can target objects of this labeling category with small areas and dark colors (for example, microvascular tumors, spotting and linear bleeding in fundus images).
  • the method for obtaining the threshold value for the label category may be determined by the first preset area and preset color values. In this way, it is possible to automatically acquire the acquisition threshold method used by the label category.
  • the label category can be It is determined to use the first method, otherwise if the average area of the label category is not greater than the first preset area and the average color is not less than the preset color value (that is, the area of the target of the label category is relatively small, and the color is relatively dark), Then the label class can be determined to use the second method.
  • the first preset area and the preset color value can be adjusted according to the result of the region segmentation.
  • the first preset area and the preset color value may be fixed values, and the fixed values may be obtained according to statistics on sample data. That is, a statistical method can be used to count the region segmentation results of a small amount of sample data under different first preset areas and preset color values to determine the best first preset area and preset color values for classification .
  • the target segmentation threshold can be obtained according to the method for obtaining the threshold value of the label category to which the target belongs.
  • the object segmentation threshold may be obtained according to the method for obtaining the threshold value of the labeled category to which the object belongs and the image data to be segmented corresponding to the training samples.
  • the threshold value can be searched to make the area of the pixel whose gray value is greater than the threshold value in the image data to be segmented If it is smaller than a preset multiple of the area of the image data to be segmented, the threshold is used as the target segmentation threshold, wherein the preset multiple may be greater than 0 and less than 1.
  • the threshold value from 0 to 255 can be traversed to find the threshold value so that the area of the pixel whose gray value is greater than the threshold value in the image data to be segmented is smaller than the preset multiple of the area of the image data to be segmented.
  • the preset multiple can be any value that makes the target area not divided.
  • the preset multiplier can take a smaller value so that the target area is not divided.
  • the preset multiplier may be empirically determined by the shape of the target.
  • the mean value of the gray value of the pixels in the image data to be segmented can be used as the target segmentation threshold or based on The gray values of the four corner regions and the central region of the image data to be segmented determine the target segmentation threshold.
  • the preset length can be any value that prevents the target area from being segmented.
  • the preset length may be a first preset ratio of the smallest side of the training sample. Specifically, the preset length can be represented as min(rH, rW), where r can represent the first preset ratio, H can represent the height of the training sample, and W can represent the width of the training sample.
  • the first preset ratio may be a fixed value. In some examples, the first preset ratio may be determined according to an average size of objects of the same category in the medical image. In some examples, the first preset ratio may be determined according to the average width and average height of objects of the same category in the medical image. In some examples, the first preset ratio may satisfy the formula:
  • the medical image may be an image in the data source used to obtain the first preset ratio.
  • the data source can be training data.
  • related parameters related to the first preset ratio may be acquired in a manner similar to related parameters related to acquiring preset hyperparameters, which will not be repeated here.
  • the target can be determined based on the gray values of the four corner areas and the central area of the image data to be segmented Segmentation threshold.
  • the image data to be segmented can be equally divided into a preset number of parts (for example, 9 equal parts), and the target segmentation threshold can be determined based on the gray values of the four corner regions and the central region of the image data to be segmented.
  • the target segmentation threshold can be determined based on the gray values of the four corner regions and the central region of the image data to be segmented.
  • a training set may be constructed using training samples and region segmentation results. That is, the training set may be constructed based on the training samples and at least one region segmentation result corresponding to the training samples.
  • the training set can include training samples and a gold standard of training samples.
  • a gold standard of training samples can be obtained based on the region segmentation results. That is, the target region can be identified based on the region segmentation result, and then the real category to which the pixels in the training samples belong can be determined based on the target region. Thereby, the gold standard of training samples can be obtained.
  • the ground-truth category may include at least one of annotated categories of objects (for example, for fundus images, may include microangioma, spotting, sheet-like bleeding, and linear bleeding), no-target category, and undetermined category . Specifically, it is related to the process of optimizing the model to be trained 130 .
  • the labeled category of the object in the real category may be the category to which the pixels of the target area (ie, the second area described later) of the object in the labeled area in the training sample belong.
  • the undetermined category in the true category may be the category to which the pixels of the region other than the target region (that is, the first region described later) of the object in the labeled region in the training sample belong.
  • the non-target category in the ground truth category can be the category to which the pixels outside the labeled region in the training samples belong.
  • the areas outside the labeled area in the training samples may include areas within the ROI that do not belong to the labeled area (ie, the third area described later).
  • the region within the region of interest and not belonging to the marked region may be the region corresponding to the non-target tissue in the medical image.
  • the areas outside the marked area in the training samples may include areas within the ROI that do not belong to the marked area, and areas outside the ROI (ie, the fourth area described later).
  • a validation set and a test set can also be constructed using the training samples and the region segmentation results.
  • the model training method may further include step S106.
  • step S106 the model to be trained 130 may be trained based on the training set, and the model to be trained 130 may be optimized using a training loss function.
  • the model to be trained 130 may include, but is not limited to, a semantic segmentation model.
  • the prediction results of the model to be trained 130 may include, but not limited to, semantic segmentation results of training samples.
  • small objects can be identified.
  • the prediction result may be the semantic segmentation result of the image data.
  • the above-mentioned input 120 may be color image data.
  • high-dimensional feature information can be added to the model to be trained 130 .
  • the accuracy of recognition of small objects can be improved.
  • feature information of different dimensions in medical images such as training samples
  • the feature information of a preset dimension close to the feature information of the highest dimension can be combined with the feature information of the highest dimension Fusion is performed to increase high-dimensional feature information.
  • FIG. 6 is an architecture diagram showing an example of a model to be trained 130 employing a U-Net architecture involved in an example of the present disclosure.
  • FIG. 6 shows a model 130 to be trained using the U-Net architecture, wherein, for the common network layers in the U-Net architecture, too much explanation is not given here.
  • the preset dimension can be 2
  • the feature information of the two dimensions can include feature information 131a and feature information 132b, wherein the feature information 131a can be fused with the feature information of the highest dimension through the upsampling layer 132a, and the feature information The information 131b can be fused with the feature information of the highest dimension through the upsampling layer 132b.
  • the convolution size of the upsampling layer 132a and the upsampling layer 132b may be any value that makes feature information (for example, feature information 131a and feature information 131b ) consistent with the size of the feature information of the highest dimension after upsampling.
  • the prediction result corresponding to the training sample can be obtained based on the training sample of the training set by the model to be trained 130, and then the training loss function can be constructed based on the region segmentation result and the prediction result corresponding to the training sample (That is, the training loss function can be constructed using the gold standard and prediction results of the training samples obtained based on the region segmentation results).
  • the training loss function can represent the degree of difference between the gold standard of the training sample and the corresponding prediction result.
  • the region segmentation results can be directly used as the gold standard for training samples.
  • the region segmentation result may be used as the gold standard of the pixels in the labeled region corresponding to the target in the training sample to obtain the gold standard of the training sample.
  • the gold standard of pixels in areas other than the marked area corresponding to the target in the training sample can be set as required. For example, it may be fixed as a category (for example, it may be the no-target category involved in the example of the present disclosure). For another example, it may be set by manually labeling the training samples or automatically labeling the training samples by an artificial intelligence algorithm. The example of the present disclosure does not specifically limit the method of setting the gold standard of the pixels in the region other than the marked region corresponding to the target in the training sample.
  • weights may be assigned to the aforementioned pixels of undetermined categories in the training samples to reduce the negative impact of the pixels of undetermined categories on the model 130 to be trained.
  • accuracy of the model to be trained 130 can be improved.
  • spatial weights can be used to reduce the negative impact of pixels of undetermined categories in the training samples on the model 130 to be trained.
  • the training sample may be divided into several regions (also referred to as at least one region), and weights are used to adjust the influence of each of the several regions on the model 130 to be trained.
  • the number of regions may include the first region.
  • the first area may be an area of pixels of undetermined categories in the training sample (that is, an area other than the target area in the marked area in the training sample).
  • spatial weights may be used to reduce the negative impact of pixels in the first region of the training samples on the model to be trained 130 .
  • the pixels in the first region in the training samples may be assigned a first weight to reduce the negative impact on the model 130 to be trained.
  • the first weight may be any value that reduces the negative impact on the model 130 to be trained.
  • the first weight may be a fixed value.
  • the first weight may be zero. In this case, samples of undetermined categories can be ignored, so as to reduce the negative impact of samples of undetermined categories on the model 130 to be trained.
  • the number of regions may include the second region.
  • the second area may be the target area of the training samples.
  • pixels of the second region may be assigned a second weight in the spatial weights.
  • the first weight may be less than the second weight.
  • the second weight may be any value that increases the positive influence of the pixels in the second region on the model 130 to be trained.
  • the second weight can be a fixed value.
  • the second weight may be one.
  • the number of regions may include a third region.
  • the third region may be a region in the region of interest in the training sample that does not belong to the labeled region.
  • pixels of the third region may be assigned a third weight in the spatial weights.
  • the first weight may be less than the third weight.
  • the principle of setting the third weight may be similar to that of the second weight.
  • the number of regions may include a fourth region.
  • the fourth area may be an area outside the area of interest in the training sample.
  • pixels of the fourth region may be assigned a fourth weight in the spatial weights.
  • the fourth weight may be less than the second weight.
  • the principle of setting the fourth weight may be similar to that of the first weight.
  • several areas may include the first area, the second area, the third area and the fourth area at the same time, and the pixels in the first area, the second area, the third area and the fourth area may be respectively assigned the first weight, second weight, third weight and fourth weight, wherein the first weight may be smaller than the second weight and smaller than the third weight, and the fourth weight may be smaller than the second weight and smaller than the third weight.
  • the first weight may be smaller than the second weight and smaller than the third weight
  • the fourth weight may be smaller than the second weight and smaller than the third weight.
  • the first weight may be 0, the second weight may be 1, the third weight may be 1, and the fourth weight may be 0.
  • the accuracy of the model to be trained 130 can be improved.
  • the several areas may include any combination of the first area, the second area, the third area and the fourth area.
  • FIG. 7 is a schematic diagram illustrating several areas of some examples to which examples of the present disclosure relate.
  • FIG. 7 is a schematic diagram showing various binarized regions, and does not limit the present disclosure to be divided into all the regions shown in FIG. 7 .
  • D3 may represent the first area
  • D4 may represent the second area
  • D5 may represent the third area
  • D6 may represent the fourth area.
  • the training sample in the spatial weighting, can be divided into several regions, and the influence of each region in the several regions can be adjusted by using the weights to the model 130 to be trained.
  • losses may be computed per class.
  • the ground-truth category may include at least one of the labeled category of the target, the non-target category, and the undetermined category.
  • the classes can be derived from the ground-truth classes described above. That is, the categories in the training loss function may include the labeled category of the target and the non-target category, or the categories in the training loss function may include the labeled category of the target, the non-target category, and the undetermined category.
  • the categories in the specific training loss function are related to the samples selected in the training loss function.
  • the training loss function if samples (that is, pixels) of each category in the training samples belong to corresponding regions among several regions, the loss of the corresponding samples may be multiplied by the weight of the corresponding region.
  • the training loss function can be determined based on the spatial weights, and then the influence of pixels in different regions on the model to be trained 130 can be adjusted.
  • the influence of the samples of each category on the model 130 to be trained can be adjusted based on the weight of each category.
  • the influence of samples to be trained on the model 130 can be adjusted based on both spatial weights and category weights.
  • the influence of samples on the model to be trained 130 can be adjusted by region and class.
  • the training loss function may employ weighted balanced cross-entropy. In this case, the imbalance between positive and negative samples can be suppressed, thereby further improving the recognition accuracy of the model 130 to be trained for small targets.
  • a training loss function based on weighted equalization cross entropy may be used, and spatial weights may be used to control the negative impact of pixels of undetermined categories on the model to be trained 130 .
  • the first weight of the first area is 0, the second weight of the second area is 1, the third weight of the third area is 1, and the fourth weight of the fourth area is 0 as an example.
  • the description is based on Weighted balanced cross-entropy training loss function. It should be noted that this does not represent a limitation to the disclosure, and those skilled in the art can design a training loss function based on weighted balanced cross-entropy by freely combining the weights of each area and each category in several areas according to the situation.
  • the training loss function L based on weighted balanced cross-entropy can satisfy the formula (that is, it is equivalent to ignoring the losses of the first area and the fourth area by setting the first weight and the fourth weight to 0):
  • C can represent the number of categories
  • W i can represent the weight of the i-th category
  • M i can represent the number of samples of the i-th category
  • y ij can represent the jth of the i-th category in the gold standard of the above training samples.
  • the actual value of the sample, p ij can represent the predicted value of the jth sample of the i-th category in the prediction result (that is, the probability that the j-th sample belongs to the i-th category).
  • the samples of each category may be pixels of the corresponding category in the training samples.
  • the samples of a category can be determined based on the above-mentioned gold standard of the training samples.
  • the weight of the category can adjust the impact of samples of each category on the model 130 to be trained.
  • the categories in the training loss function can include the label category of the target and the non-target category, and the target The labeled category may be the category to which the pixels in the second region in the training sample belong, and the non-target category may be the category to which the pixels in the third region in the training sample belong.
  • the categories in the training loss function of Equation (1) can include microangioma, spot hemorrhage, sheet hemorrhage and linear hemorrhage and no target category.
  • FIG. 8 is a flowchart illustrating an example of a method of recognizing an object in an image according to an example of the present disclosure.
  • the identification method may include step S302.
  • step S302 a medical image as an input image may be acquired.
  • the input image can be input into the trained model after the same preprocessing as the above-mentioned training samples.
  • the identification method may further include step S304.
  • step S304 at least one trained model can be used to determine the prediction results of each trained model for the input image, and the target prediction result can be obtained based on the prediction results of at least one trained model, wherein at least one trained model can be based on the above-mentioned Obtained by training with the model training method.
  • at least one trained model may be a model based on the same type of network architecture (eg, U-Net), but with different network structures and/or parameters. For example, some branches or network levels may be added or subtracted to form at least one trained model.
  • U-Net network architecture
  • examples of the present disclosure are not limited thereto, and in other examples, at least one trained model may not be based on the same type of network architecture.
  • the prediction results of each trained model may include the probability that each pixel in the input image belongs to the corresponding labeled category.
  • the labeling category may be the labeling category of the above-mentioned target.
  • the prediction results of at least one trained model may be integrated according to the label category and pixels to obtain the integrated probability that each pixel of the input image belongs to the corresponding label category, determine the connected region based on the integrated probability, and obtain each pixel based on the connected region.
  • the target prediction result corresponding to the labeled category In this case, obtaining the target prediction result based on the integrated probability can further improve the accuracy of the target prediction result.
  • the probability that each pixel in the input image in the predicted result of the trained model belongs to the corresponding label category can be used as the integrated probability; otherwise, the multi- The prediction results of each trained model are averaged to obtain the average probability of each pixel in the input image belonging to the corresponding label category (that is, the pixel-level probability average can be calculated according to the label category).
  • the connected region in determining the connected region based on the integrated probability, may be determined based on the integrated probability and the classification threshold of each label category. Specifically, the values of pixels whose integration probability is not less than the classification threshold can be set to 1, and the values of other pixels can be set to 0. In some examples, the classification threshold can be determined based on a validation set using performance metrics. In addition, if there are connected regions, the number of connected regions may be one or more.
  • the circumscribed rectangle of each connected region in obtaining the target prediction result based on the connected region, can be obtained. If the area of the circumscribed rectangle is greater than the second preset area, it can indicate that there is a target at the circumscribed rectangle, otherwise it can indicate that the There is no target at the bounding rectangle.
  • the second preset area may be a second preset ratio of the area of the training samples.
  • the second preset area may be expressed as sHW, where s may represent the second preset ratio, H may represent the height of the input image, and W may represent the width of the input image.
  • the second preset ratio may be a fixed value. In some examples, the second preset ratio may be determined according to the median area of objects of the same category in the medical image. In some examples, the second preset ratio s may satisfy the formula:
  • m can represent the median value of the area of the target of the same category in the medical image
  • ⁇ can represent the standard deviation of the area of the target of the same category in the medical image
  • the average width and average height of medical images can be represented respectively.
  • the medical image may be an image in the data source used to acquire the second preset ratio.
  • the data source can be training data.
  • the relevant parameters involved in the second preset ratio may be acquired in a similar manner to the related parameters involved in acquiring the preset hyperparameters, which will not be repeated here.
  • the present disclosure also relates to a computer-readable storage medium.
  • the computer-readable storage medium can store at least one instruction, and when the at least one instruction is executed by a processor, one or more steps in the above-mentioned model training method or recognition method are realized.
  • the present disclosure also relates to electronic devices, which may include at least one processing circuit.
  • At least one processing circuit is configured as one or more steps in the above-mentioned model training method or recognition method.
  • the model training, method, device and medium for identifying targets in medical images under-segment the image data in the marked area in the training sample to identify pixels of undetermined categories in the marked area, and combine the spatial weights
  • the model to be trained 130 is trained to reduce the negative impact of the pixels of undetermined categories in the labeled region on the model to be trained 130 , thereby improving the accuracy of the predicted result of the trained model 130 to the input image.
  • small targets can be effectively identified.

Abstract

L'invention concerne un procédé d'entraînement d'un modèle pour reconnaître une cible dans une image médicale, un procédé de reconnaissance d'une cible dans une image médicale, ainsi qu'un dispositif et un support. Le procédé d'entraînement de modèle consiste à acquérir une image médicale en tant qu'échantillon d'entraînement et une région marquée correspondant à une cible dans l'échantillon d'entraînement (S102) ; à déterminer un résultat de segmentation de région correspondant à la région marquée et à construire un ensemble d'entraînement à l'aide de l'échantillon d'entraînement et du résultat de segmentation de région (S104), le résultat de segmentation de région étant acquis par réalisation d'une sous-segmentation sur des données d'image dans la région marquée ; et sur la base de l'ensemble d'entraînement, à entraîner un modèle à entraîner, et à optimiser ledit modèle à l'aide d'une fonction de perte d'entraînement (S106), dans la fonction de perte d'entraînement , l'effet négatif de pixels d'une première région dans l'échantillon d'entraînement sur ledit modèle étant réduit à l'aide d'un poids spatial, la première région étant une région dans l'échantillon d'entraînement autre qu'une région cible de la cible dans la région marquée, et la région cible étant déterminée par le résultat de segmentation de région. Par conséquent, une petite cible peut être reconnue efficacement.
PCT/CN2022/095137 2022-03-02 2022-05-26 Procédé d'entraînement de modèle pour reconnaître une cible dans une image médicale, procédé de reconnaissance d'une cible dans une image médicale, et dispositif et support WO2023165033A1 (fr)

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