Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.

翻译：整合高层语义相关内容与低层解剖特征在医学图像分割中至关重要。为此，近期基于深度学习的医学分割方法在建模此类信息方面展现出巨大潜力。然而，用于医学分割的卷积算子通常作用于规则网格，这固有地模糊了高频区域（即边界区域）。本文提出MORSE，一种在解剖层面设计的通用隐式神经渲染框架，旨在辅助医学图像分割的学习。该方法的动机在于：隐式神经表示在拟合复杂信号和解决计算机图形学问题方面，相较于离散网格表示已被证明显著有效。本方法的核心是将医学图像分割公式化为一个端到端的渲染问题：具体而言，我们持续将粗糙的分割预测与基于模糊坐标的点表示对齐，并聚合这些特征以自适应地细化边界区域。为并行优化多尺度像素级特征，我们借鉴混合专家（MoE）思想，通过随机门控机制设计并训练MORSE。实验表明，MORSE能与不同医学分割骨干网络良好兼容，在2D和3D监督式医学分割方法中持续取得有竞争力的性能提升。此外，我们还从理论上分析了MORSE的优越性。