EP-4736108-A1 - ABNORMALITY DETECTION IN MEDICAL IMAGES

Abstract

A method is provided of abnormality detection in a medical image. Different regions of the image are associated with different abnormalities. Correlations are derived between the presence of the respective associated abnormalities in the different regions of a training dataset. For a target abnormality, a region of interest is defined as a combination of the regions for which a correlation of the respective associated abnormality with the target abnormality meets a threshold. That combined region of interest is then used for detecting the target abnormality in the medical image.

Inventors

• WEESE, Rolf Jürgen
• FLÄSCHNER, Nick
• WENZEL, FABIAN
• EWALD, Arne
• MARKOV, Nikita
• PADALKO, Mikhail

Assignees

• Koninklijke Philips N.V.

Dates

Publication Date

20260506

Application Date

20240620

Claims (15)

1. 1. A method of training a classifier to detect a target abnormality in a medical image, wherein the medical image comprises a plurality of regions corresponding to different anatomical features and wherein at least one of the plurality of regions has at least one associated abnormality, wherein the method comprises: receiving (10) a training dataset; using (12) the training dataset to derive correlations between the presence of the respective associated abnormalities in the different regions; for the target abnormality, defining (14) a region of interest as a combination of the regions for which a correlation of the respective associated abnormality with the target abnormality meets a threshold; and training (16) the classifier using the training dataset to detect the target abnormality using the region of interest.
2. 2. The method of claim 1, wherein the classifier comprises a neural network.
3. 3. The method of claim 1 or 2, further comprising training a second classifier using the training dataset and using the region having the target abnormality as the region of interest of the second classifier.
4. 4. A computer program comprising computer program code which is adapted, when said program is run on a computer, to implement the method of any one of claims 1 to 3.
5. 5. A method, for detecting a target abnormality in the medical image, wherein the medical image comprises a plurality of regions corresponding to different anatomical features and wherein at least one ofthe plurality of regions has at least one associated abnormality, wherein the method comprises: performing (21) segmentation of anatomical structures in the at least one of the plurality of regions and constructing regions of interest on the basis of the segmentation result; and applying (22) the classifier trained using the method of any one of claims 1-3 to the regions of interest of the medical image.
6. 6. The method of claim 5, further comprising applying a second classifier trained using a training dataset and using the at least one of the plurality of regions having the target abnormality of the second classifier.
7. 7. The method of claim 6, comprising presenting classification probabilities to the user for the classifier and for the second classifier.
8. 8. The method of any one of claims 5 to 7, representing the region of interest on which the classifier operates on.
9. 9. A training apparatus for training a classifier to detect a target abnormality in a medical image, wherein the medical image comprises a plurality of regions corresponding to different anatomical features and wherein at least one of the plurality of regions has at least one associated abnormality, wherein the apparatus comprises a processor configured to: receive a training dataset; use the training dataset to derive correlations between the presence of the respective associated abnormalities in the different regions; and for the target abnormality, define a region of interest as a combination of the regions for which a correlation of the respective associated abnormality with the target abnormality meets a threshold; and train the classifier using the training dataset to detect the target abnormality using the region of interest.
10. 10. The apparatus of claim 9, wherein the processor is further configured to train a second classifier using the training dataset and using the region having the target abnormality as the region of interest of the second classifier.
11. 11. An image analysis system for analyzing a medical image, wherein the medical image comprises a plurality of regions corresponding to different anatomical features and wherein at least one of the plurality of regions have at least one associated abnormality, the system comprising a processor configured to: perform (21) segmentation of anatomical structures in the at least one of the plurality of regions and construct regions of interest on the basis of the segmentation result; and apply (22) the classifier trained using the method of any one of claims 1-3 to the regions of interest of the medical image.
12. 12. The system of claim 11, wherein the trained classifier is a multi-class classifier with, optionally, multioutput multilabel classification.
13. 13. The system of any one of claims 11 or 12, wherein the processor is further configured to: apply a second classifier trained using the training dataset and using at least one of the plurality of the regions having the target abnormality as the region (-s) of interest of the second classifier; and present classification probabilities to the user for the classifier and for the second classifier.
14. 14. The system of claim 13, wherein the processor is configured to fuse or otherwise combine classification probabilities for the classifier and the second classifier and provide the fused classification probabilities with the classification result.
15. 15. The system of any one of claims 11 to 14, comprising a display and a display controller, wherein the display controller is configured to control the display to represent the at least one of the plurality of regions of interest on which the classifier operates.

Description

ABNORMALITY DETECTION IN MEDICAL IMAGES FIELD OF THE INVENTION This invention relates to the detection of abnormalities in medical images. For example, it relates to the analysis of medical images using AL BACKGROUND OF THE INVENTION For Al-based reporting of medical scan images, for example MR knee exams, a large number of neural network (NN) - based classifiers must be trained. Training and performance of the NN- based classifiers often improve when the region-of-interest (ROI) in the image where a finding (e.g.. an anatomical abnormality) to be detected is properly defined (e.g. by a bounding box). These findings usually relate to specific anatomical structures (e.g. meniscus, ligament, cartilage on a specific part of the bone, etc.). The ROI for investigating a finding should thus be restricted to the associated anatomical structure. A segmentation of the relevant anatomical structures is performed for example using model-based segmentation. The label mask corresponding to a specific anatomical structure and related findings is used to define a ROI that is the input to a classification network for detecting the presence of a specific finding. Although findings usually refer to specific structures, in various cases a finding in one structure strongly correlates with a finding in another structure. For instance, cartilage damage correlates with bone subchondral edema. For those cases, restricting the region-of-interest to the primary anatomical structure related to the finding can lead to suboptimal classification results. There is therefore a need for an improved way to detect a target abnormalities (i.e., a target finding) using machine learning. SUMMARY OF THE INVENTION The invention is defined by the claims. According to examples in accordance with an aspect of the invention, there is provided a method of training a classifier to detect a target abnormality in a medical image, wherein the medical image comprises a plurality of regions corresponding to different anatomical features and wherein at least one region of the plurality of regions has at least one associated abnormality, wherein the method comprises: receiving a training dataset; using the training dataset to derive correlations between the presence of the respective associated abnormalities in the different regions; and for the target abnormality, defining a region of interest as a combination of the regions for which a correlation of the respective associated abnormality with the target abnormality meets a threshold; and training the classifier using the training dataset to detect the target abnormality using the region of interest This method relates to the training of the classifier for subsequent use in detecting the target abnormality in a medical image. This method exploits correlations between detected abnormalities in different anatomical features in order to define the region of interest used for finding a specific target abnormality. To derive the correlations, an anatomical segmentation is performed using neural networks, model-based segmentation or other techniques that label the anatomical features (e.g. meniscus, ligament, cartilage on a specific part of the bone, ... ) for which abnormalities should be detected. Correlations between those abnormalities can then be analyzed in the dataset. When there is a high correlation (above a threshold) of the target abnormality with other abnormalities, the regions are merged (e.g., anatomical label masks are merged) to define the region of interest for detecting the target abnormality. Thus, rather than detecting a target abnormality using the segmented image region for the particular anatomical feature primarily associated with that abnormality, other regions are also used in which other abnormalities correlate with the target abnormality. If there is no correlation with other abnormalities, the region of interest will remain as the single region primarily associated with the target abnormality. The correlation information may also be used to decide whether independent classifiers are used for different abnormalities (e.g., when there is no or little correlation) or a multi-class classifier should be used allowing to detect multiple findings (e.g., when there is strong correlation between findings). The trained classifier is then used to detect a target abnormality. In this way, the detection performance for identifying the target abnormality is optimized. The classifier for example comprises a neural network. The method may further comprise training a second classifier using the training dataset and using the region having the target abnormality as the region of interest of the second classifier. In this way, classifiers are trained based on a fused region of interest as well as based on a single region of interest of a single anatomical feature. In this way, the user can be presented with multiple classification results which have been derived in different ways. The invention also