Medical Image Segmentation Using Iterative User Feedback
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Solution Overview
Problem
Existing medical image segmentation models struggle to accurately segment target regions, particularly those without clear tissue boundaries, requiring professional domain knowledge and multiple interactions to meet clinical needs.
Innovation Solution
A system and method for medical image segmentation that incorporates user interaction through an iterative process, allowing users to modify images within an image segmentation model, terminating the iteration when the modified image meets predefined conditions, and using these interactions to update the model's parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If an image segmentation model is used to automatically segment target regions, then the segmentation process is automated and efficient, but the segmentation accuracy is insufficient for target regions without obvious tissue boundaries
Solution Approach 1:
The system implements an iterative feedback mechanism where the segmentation model generates preliminary results, users provide corrections or confirmations, and the model updates its parameters based on this feedback. This closed-loop process allows the automated model to progressively improve accuracy by learning from user interactions, particularly for challenging target regions without clear boundaries.
Solution Approach 2:
The segmentation model performs self-updating of its parameters based on user interaction data. Instead of requiring manual retraining with extensive datasets, the model automatically adjusts its segmentation strategy by learning from individual user corrections, enabling it to adapt to specific user preferences and clinical needs without external intervention.
2Measurement precision
If user interaction is incorporated into the segmentation process to improve accuracy, then segmentation precision improves, but the operation complexity and time consumption increase
Solution Approach 1:
The system implements a selective interaction approach where users only need to provide input when the automated segmentation is insufficient or when higher precision is required. For routine cases with clear boundaries, the model operates autonomously without user intervention, while complex cases trigger optional user feedback, balancing accuracy improvements with operational simplicity.
3Adaptability or versatility
If extensive training samples are used to improve the segmentation model's performance, then the model's generalization ability improves, but the time and resources required for training increase
Solution Approach 1:
The system performs preliminary training with a small initial dataset to establish baseline performance, then continuously adapts to individual users through incremental learning during actual usage. This approach eliminates the need for extensive pre-training by distributing the learning process over time, allowing the model to achieve high adaptability through repeated exposure to user-specific data in a lightweight, ongoing manner.
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.