JP-7856215-B2 - Detection of anatomical abnormalities in 2D medical images

Inventors

• フォン ベルグ イェンス
• ヤング スチュワート マシュー
• ヘルトガース オマール
• ブルーク ハイナー マシアス
• クロンケ ヒル スヴェン
• ビストロフ ダニエル
• フーセン アンドレ
• ハーダー ティム フィリップ

Assignees

• コーニンクレッカ フィリップス エヌ ヴェ

Dates

Publication Date

20260511

Application Date

20230823

Priority Date

20220902

Claims (15)

1. In a computer-based method for detecting anatomical abnormalities in 2D medical images using a processor , The processor performs the steps of acquiring the 2D medical image, The processor performs the steps of detecting the 2D contour of an anatomical feature, The processor takes the step of acquiring a 3D model representing the anatomical features, The step of the processor predicting the 2D contour of the anatomical features based on the 3D model, the step of predicting the 2D contour of the anatomical features based on the 3D model, A step of estimating the posture of the anatomical features from the 2D medical image, A step of adjusting the 3D model based on the estimated posture, A step of generating a 2D projection from the adjusted 3D model, and a step of predicting the 2D contour of the anatomical features from the 2D projection. Steps including, The processor performs the steps of detecting the anatomical abnormality based on the detected 2D contour and the predicted 2D contour, A method of having.
2. The step of detecting the 2D contour is, The step of segmenting the aforementioned 2D medical image, The method according to claim 1, including the method described in claim 1.
3. The processor performs the step of classifying the anatomical abnormalities. The method according to claim 1, further comprising:
4. The method according to claim 3, wherein the step of classifying the anatomical abnormalities is performed by a machine learning algorithm.
5. The aforementioned 3D model is The aforementioned 2D medical image, The aforementioned anatomical features, and/or user input, The method according to claim 1, obtained based on the present invention.
6. The aforementioned 3D model, Computer-aided design models, and reference models constructed from 3D medical images of the anatomical features in at least one posture. The method according to claim 1, wherein the method is any one of the following.
7. The detected 2D contour and, The predicted 2D contour line and, A step to generate the difference between, The method according to claim 1, further comprising:
8. The aforementioned anatomical features, Bone tissue, and ligamentous tissue, The method according to claim 1, wherein the method is any one of the following.
9. The aforementioned anatomical abnormalities Fractures and ligament tears, The method according to claim 1, wherein the method is any one of the following.
10. The step of classifying the aforementioned anatomical abnormalities is: Weber classification, Pauwels classification, Smith and Colles classification, Salter-Harris classification, and AO/OTA classification, The method according to claim 3, wherein the anatomical abnormality is classified based on any one of the following.
11. The processor generates treatment advice based on the detected anatomical abnormalities. The method according to claim 1, further comprising:
12. The method according to claim 1, wherein the medical image is an X-ray image.
13. A computer program having instructions for enabling a processor to perform the method described in any one of claims 1 to 12.
14. A system for detecting anatomical abnormalities in 2D medical images, comprising a processor configured to perform the method described in any one of claims 1 to 12.
15. The system according to claim 14, further comprising an X-ray source and an X-ray detector.

Description

This invention relates to a computer-based method, a computer program product, and a system configured to detect anatomical abnormalities in 2D medical images. Medical images are often used for diagnostic purposes. For example, medical images are examined by a human observer for anatomical abnormalities. Examples of such abnormalities include fractures and tumor tissue, which can be identified in X-rays, ultrasounds, CT (computed tomography), or other medical images. However, examination of medical images by human observers is time-consuming and prone to errors, depending on the observer's expertise, experience, time constraints, and fatigue. This is especially true in the demanding environment of a busy clinic and when anatomical abnormalities are not easily identifiable. U.S. Patent Application Publication No. 2022/0044041 discloses an algorithm that provides guidance on methods for detecting, classifying, and treating bone fractures. A flowchart using One Halogen is shown.A medical image according to an embodiment of the present invention is shown.This shows the detected contours of anatomical features in medical images according to an embodiment of the present invention.This shows the predicted contours of anatomical features in medical images according to one embodiment of the present invention.The posture and field of view parameters of an imaged object are shown using one embodiment of the present invention.This shows a 3D model of the ankle joint according to an embodiment of the present invention.This shows the classification of ankle fractures according to embodiments of the present invention.This shows the classification of femoral neck fractures according to embodiments of the present invention.This describes the classification of fractures passing through the epiphysis or growth plate of a bone according to embodiments of the present invention.This shows a processor circuit according to an embodiment of the present invention. The present invention is described herein with reference to the drawings. The description and specific examples provided illustrate exemplary embodiments, but should be understood to be for illustrative purposes only and not intended to limit the scope of the invention. Furthermore, the drawings are schematic diagrams and are not drawn to scale. Also, the same reference numerals are used throughout the drawings to indicate the same or similar parts. This invention provides a computer-aided method for analyzing and evaluating 2D (two-dimensional) medical images for the purpose of identifying anatomical abnormalities. Figure 1 shows an exemplary flowchart of an embodiment of Method 100 according to the present invention, which includes the following steps. In step 110, a 2D medical image 210 is acquired. The 2D medical image may include, but is not limited to, any other type of medical image, such as an X-ray image, a CT (computed tomography) image, an ultrasound image, an MR (magnetic resonance) image, or any other type of medical image. An exemplary medical image 210 envisioned in this invention is a 2D X-ray image of the ankle joint 211 shown in Figure 2A. In step 120, 2D contours of anatomical features are detected. This may include, for example, segmentation of 2D medical images or an end-to-end machine learning approach. Generally, segmentation can be understood as the task of classifying an image pixel by pixel with labels. Alternatively, the classification may be based on larger image regions as a whole, such as contours or surfaces. Labels represent the semantics of each pixel or image region. In this way, in the classification operation, for example, bone pixels, i.e., pixels with the label "bone," can be identified. In this case, the pixels are considered to represent bone tissue, and so are other tissue types. Thus, the footprints of organs in an image can be identified by segmentation. In alternative embodiments, segmentation may be performed using a neural network or convolutional neural network, such as those disclosed in WO 2022/084074. Algorithms including prior shape knowledge, such as those disclosed therein, are also assumed by the present invention. From image segmentation, a segmented image is typically obtained, where each pixel and/or region has a specific label assigned to it. This label may be binary "bone/non-bone" or multilevel "bone/ligament/non-bone/...". According to one embodiment of the present invention, a portion of the medical image corresponding to an anatomical feature of interest, e.g., "bone" or more specifically "tibia/fibula/calcaneus/talus," may be selected to generate a 2D contour 212, as shown in Figure 2B. In step 130, a 3D (three-dimensional) model 330 representing the imaged object 211 or identified anatomical feature 213 is acquired. The model is -Medical image 210 acquired in step 110, - The anatomical features identified in step 120, and - A 3D model may be selected from a list based on at least one of the user inputs. For e