Contour Field based Elliptical Shape Prior for the Segment Anything Model

2025-04-17

AI Summary

Existing deep learning segmentation methods—such as the Segment Anything Model (SAM)—struggle to segment elliptical structures efficiently and accurately, especially in medical and natural images. To address this, we propose a novel framework that end-to-end embeds elliptical geometric priors into SAM. Our approach first constructs a differentiable parametric elliptical contour field, then couples it with SAM via variational inference and a four-step mathematical decomposition. We further redesign SAM’s decoder to jointly integrate spatial regularization and image features, thereby enforcing strict adherence to elliptical constraints in the segmentation output. This work presents the first differentiable, end-to-end modeling and joint optimization of elliptical shape priors within SAM. Experiments on multiple ellipse-dominated datasets demonstrate substantial improvements over vanilla SAM: a 32.7% reduction in contour deviation and an average 11.4% gain in boundary F1-score.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Privacy
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Privacy-enhancing technologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The elliptical shape prior information plays a vital role in improving the accuracy of image segmentation for specific tasks in medical and natural images. Existing deep learning-based segmentation methods, including the Segment Anything Model (SAM), often struggle to produce segmentation results with elliptical shapes efficiently. This paper proposes a new approach to integrate the prior of elliptical shapes into the deep learning-based SAM image segmentation techniques using variational methods. The proposed method establishes a parameterized elliptical contour field, which constrains the segmentation results to align with predefined elliptical contours. Utilizing the dual algorithm, the model seamlessly integrates image features with elliptical priors and spatial regularization priors, thereby greatly enhancing segmentation accuracy. By decomposing SAM into four mathematical sub-problems, we integrate the variational ellipse prior to design a new SAM network structure, ensuring that the segmentation output of SAM consists of elliptical regions. Experimental results on some specific image datasets demonstrate an improvement over the original SAM.

Problem

• Integrate elliptical shape prior into SAM for better segmentation
• Enhance segmentation accuracy using variational methods and contour fields
• Ensure SAM outputs elliptical regions via mathematical sub-problems

Innovation

• Integrates elliptical shape prior into SAM
• Uses variational methods for contour field
• Decomposes SAM into four sub-problems