GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation
Journal:
arXiv
Published Date:
Jan 22, 2025
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.
