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

TL;DR

GAMED-Snake introduces a gradient-aware, contour-based framework for multi-organ segmentation that couples a Distance Energy Map Prior with a Differential Convolution Inception Module and an Adaptive Momentum Evolution Mechanism. The method initializes contours from a CenterNet detector and iteratively refines them using energy-guided gradients and cross-attention-based momentum across evolution steps, improving robustness to complex backgrounds and blurred boundaries. Across four challenging datasets, GAMED-Snake achieves notable gains in mIoU and mDice over state-of-the-art semantic segmentation models, with ablations confirming the additive benefits of DEMP, DCIM, and AMEM. The approach offers a robust, anatomy-aware alternative that complements pixel-wise methods and holds promise for clinical workflows, with code to be released publicly.

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.

Figures (7)

• Figure 1: (a) The workflow of GAMED-Snake consists of two stages: initialization of detection boxes and contour evolution. Taking the detection boxes as the initial contours, snake evolution process iteratively deforms them to match organ boundaries. (b) Semantic segmentation models based on pixel classification often struggle with complex multi-organ segmentation scenes, resulting in errors as illustrated in Fig. \ref{['fig:image1']}(b). In contrast, snake algorithms inherently avoid these issues, producing smooth and precise contours. (c) Improvement of GAMED-Snake over the SOTA approaches on MR_AVBCE Zhao2023Attractive and BTCV landman2015miccai datasets.
• Figure 2: (a) The pipeline of GAMED-Snake: GAMED-Snake first generates initial contours and then deforms them to align with the target boundaries under the guidance of energy maps. (b) The principles underlying the Distance Energy Map: This map encodes the distance distribution to guide contour evolution effectively. (c) The structure of the Adaptive Momentum Evolution Mechanism (AMEM): AMEM adaptively integrates current and historical state information, establishing dynamic features across different iterations of evolution.
• Figure 3: (a) The working principle of differential convolution. (b) The structure of the Differential Convolution Inception Module (DCIM). (c) The process of energy gradient extraction. We aggregate feature information near contour point locations with DCIM to guide the snake evolution process.
• Figure 4: Qualitative comparison of results on MR_AVBCE datasets.
• Figure 5: Qualitative comparison of results on BTCV datasets.