  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.
