AIC-UNet: Anatomy-informed Cascaded UNet for Robust Multi-Organ
  Segmentation
Young Seok Jeon Hongfei Yang Huazhu Fu Mengling Feng
Imposing key anatomical features, such as the number of organs, their shapes,
sizes, and relative positions, is crucial for building a robust multi-organ
segmentation model. Current attempts to incorporate anatomical features include
broadening effective receptive fields (ERF) size with resource- and
data-intensive modules such as self-attention or introducing organ-specific
topology regularizers, which may not scale to multi-organ segmentation problems
where inter-organ relation also plays a huge role. We introduce a new approach
to impose anatomical constraints on any existing encoder-decoder segmentation
model by conditioning model prediction with learnable anatomy prior. More
specifically, given an abdominal scan, a part of the encoder spatially warps a
learnable prior to align with the given input scan using thin plate spline
(TPS) grid interpolation. The warped prior is then integrated during the
decoding phase to guide the model for more anatomy-informed predictions. Code
is available at
\hyperlink{https://anonymous.4open.science/r/AIC-UNet-7048}{https://anonymous.4open.science/r/AIC-UNet-7048}.