GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

2025-01-22

AI Summary

To address inaccurate contour localization in multi-organ segmentation caused by complex anatomical structures and ambiguous boundaries, this paper proposes a deep snake model based on contour evolution. Methodologically, we introduce a novel Distance Energy Map Prior (DEMP) to explicitly model boundary uncertainty; design a Differential Convolutional Inception Module (DCIM) to enhance multi-scale gradient perception; and propose an Adaptive Momentum Evolution Mechanism (AMEM) driven by cross-iteration cross-attention for robust contour optimization. Evaluated on four public multi-organ datasets, our method achieves an average mDice improvement of approximately 2.0%, significantly enhancing segmentation accuracy—particularly for morphologically diverse organs and low-contrast boundaries. The resulting segmentation outputs are both interpretable and highly robust, establishing a new paradigm for clinical applications such as precision radiotherapy planning and surgical navigation.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies. This study introduces the Gradient-aware Adaptive Momentum Evolution Deep Snake (GAMED-Snake) model, which establishes a novel paradigm for contour-based segmentation by integrating gradient-based learning with adaptive momentum evolution mechanisms. The GAMED-Snake model incorporates three major innovations: First, the Distance Energy Map Prior (DEMP) generates a pixel-level force field that effectively attracts contour points towards the true boundaries, even in scenarios with complex backgrounds and blurred edges. Second, the Differential Convolution Inception Module (DCIM) precisely extracts comprehensive energy gradients, significantly enhancing segmentation accuracy. Third, the Adaptive Momentum Evolution Mechanism (AMEM) employs cross-attention to establish dynamic features across different iterations of evolution, enabling precise boundary alignment for diverse morphologies. Experimental results on four challenging multi-organ segmentation datasets demonstrate that GAMED-Snake improves the mDice metric by approximately 2% compared to state-of-the-art methods. Code will be available at https://github.com/SYSUzrc/GAMED-Snake.

Problem

• Medical Imaging
• Organ Segmentation
• Accuracy Improvement

Innovation

• GAMED-Snake
• Gravitational Field Localization
• Dynamic Adjustment for Organ Boundary Detection