Interactive Medical Image Segmentation for Unclear Tissue Boundaries
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Solution Overview
Problem
Existing medical image segmentation models struggle to accurately segment target regions without clear tissue boundaries, necessitating clinician interaction to improve segmentation results.
Innovation Solution
An iterative process involving user interactions with an image segmentation model, where user modifications are incorporated to refine the segmentation, allowing the model to converge on a target image that meets user requirements, and the model is updated based on these interactions to adapt to specific user habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an image segmentation model is used to segment target regions, then segmentation results can be obtained, but the segmentation accuracy is insufficient when there is no obvious tissue boundary
Solution Approach 1:
The system implements an iterative feedback mechanism where the segmentation model processes an image, the clinician reviews and modifies the segmentation result, and the model uses these modifications to generate improved results in subsequent iterations. This feedback loop continues until the segmentation accuracy is satisfactory, allowing the system to adapt to complex cases without clear tissue boundaries through continuous refinement based on clinical expertise.
2Measurement precision
If clinician interaction is introduced to improve segmentation, then segmentation accuracy improves, but the operation complexity increases
Solution Approach 1:
The system automatically manages the iterative segmentation process, including generating initial segmentation results, evaluating them against predefined criteria, and preparing modified versions for clinician review. The system handles the technical complexity of model iterations, data processing, and result evaluation, allowing clinicians to focus on providing guidance rather than managing the computational process.
3Adaptability or versatility
If user modifications are incorporated into the segmentation model, then the model adapts to user habits, but the training time increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing user modifications during the segmentation process, then using these stored modifications to retrain the segmentation model. This allows the model to learn and adapt to specific user habits and preferences through the iterative process, improving adaptability while managing training time efficiently by using only the necessary modification data.
Data Source
AI summary
A method may include obtaining a first image associated with an image to be segmented, and performing an iteration process for obtaining a target image. The iteration process may include one or more iterations each of which includes: obtaining an image to be modified; obtaining one or more modifications performed on the image to be modified; generating a second image by inputting the image to be segmented, the image to be modified, and the one or more modifications into the image segmentation model; in response to determining that the second image satisfies the first condition, terminating the iteration process by determining the second image as the target image; or in response to determining that the second image does not satisfy the first condition, initiating a new iteration of the iteration process by determining the second image as the image to be modified.