Zero-Shot Medical Image Segmentation with Edge and Region Enhancement
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Solution Overview
Problem
Medical image segmentation, particularly of internal anatomy, is a time-consuming task for professionals and radiologists, and existing methods face challenges in accurately and efficiently segmenting complex anatomical structures without requiring extensive human intervention.
Innovation Solution
A medical imaging method utilizing a zero-shot image segmentation module that applies edge and region enhancement algorithms to medical images, followed by a zero-shot image segmentation process to automatically generate segmentation masks, leveraging techniques like Laplacian of Gaussian and watershed transforms, and machine learning models such as Vision Transformers, to identify and segment regions within the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual segmentation methods are used by medical professionals, then segmentation accuracy can be maintained through expert judgment, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary actions by automatically generating edge-enhanced images and region-enhanced images with location points before the final segmentation step. This preprocessing prepares the data in advance, allowing the zero-shot segmentation model to work more efficiently and accurately without requiring manual intervention during the actual segmentation process.
Solution Approach 2:
The patent introduces an intermediary processing pipeline between the raw medical image and the final segmentation result. This intermediary system includes edge enhancement algorithms, region generation algorithms, and location identification methods that transform the original image into a processed form that the zero-shot segmentation module can handle more effectively, thereby improving both accuracy and efficiency.
2Extent of automation
If zero-shot image segmentation is applied directly to raw medical images, then automation is achieved, but segmentation accuracy deteriorates due to lack of feature enhancement
Solution Approach 1:
Before applying zero-shot image segmentation, the system performs preliminary enhancement actions including edge detection, region generation, and location point identification. These preliminary actions prepare the medical image by highlighting important features and structures, enabling the automated zero-shot segmentation to achieve high accuracy without manual intervention.
Solution Approach 2:
The patent introduces an intermediary processing pipeline between the raw medical image and the final segmentation result. This intermediary system includes edge enhancement algorithms, region generation algorithms, and location identification methods that transform the original image into a processed form that the zero-shot segmentation module can handle more effectively, thereby improving both accuracy and efficiency.
3Measurement precision
If extensive preprocessing and enhancement algorithms are applied, then segmentation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct modular components: edge enhancement algorithm, region generation algorithm, location identification method, and zero-shot segmentation module. Each component performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining high segmentation accuracy through the coordinated work of these specialized modules.
Data Source
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AI summary
Disclosed herein is a medical imaging method. The method comprises: receiving (200) a medical image (124) descriptive of an internal anatomy of a subject (318); generating (202) an edge enhanced image (128) by applying an edge enhancement algorithm (126) to the medical image; generating (204) a region enhanced image (130) by applying a region generation algorithm (130) to the edge enhanced image; generating (206) a set of location points (136) uniquely identifying regions within the region enhanced image, the generating comprising applying a predetermined location identification method to the region enhanced image; receiving (208) one or more segmentation masks (138) from the zero-shot image segmentation module in response to inputting the medical image and the set of location points into a zero-shot image segmentation module (122); and providing (210) the one or more segmentation masks as a set of image segmentations of the medical image.