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 this https URL.
Abstract (translated)
多器官分割是一个既关键又具挑战性的任务，原因在于复杂的解剖背景、模糊的边界以及多样化的形态。本研究提出了基于梯度感知自适应动量演化深度蛇（Gradient-aware Adaptive Momentum Evolution Deep Snake, GAMED-Snake）模型，该模型通过将基于梯度的学习与自适应动量演化机制相结合，建立了一种新的轮廓导向分割范式。 GAMED-Snake 模型包含三大创新： 首先，距离能量图先验（Distance Energy Map Prior, DEMP）生成了一个像素级别的力场，能够有效吸引轮廓点向真实的边界靠近，在复杂背景和模糊边缘的情况下效果尤为显著。 其次，差分卷积入模块（Differential Convolution Inception Module, DCIM）可以精确提取全面的能量梯度信息，从而极大地提高了分割的准确性。 第三，自适应动量演化机制（Adaptive Momentum Evolution Mechanism, AMEM）利用跨注意力机制来建立不同进化迭代之间的动态特征，使各种形态下的边界对齐更加精准。 实验结果显示，在四个具有挑战性的多器官分割数据集上，GAMED-Snake 模型相比最先进的方法将 mDice 评价指标提高了约2%。代码将在 [此处](https://example.com) 提供（请使用实际链接）。
URL
https://arxiv.org/abs/2501.12844
