  Accurate and robust abdominal multi-organ segmentation from CT imaging of
different modalities is a challenging task due to complex inter- and
intra-organ shape and appearance variations among abdominal organs. In this
paper, we propose a probabilistic multi-organ segmentation network with
hierarchical spatial-wise feature modulation to capture flexible organ semantic
variants and inject the learnt variants into different scales of feature maps
for guiding segmentation. More specifically, we design an input decomposition
module via a conditional variational auto-encoder to learn organ-specific
distributions on the low dimensional latent space and model richer organ
semantic variations that is conditioned on input images.Then by integrating
these learned variations into the V-Net decoder hierarchically via spatial
feature transformation, which has the ability to convert the variations into
conditional Affine transformation parameters for spatial-wise feature maps
modulating and guiding the fine-scale segmentation. The proposed method is
trained on the publicly available AbdomenCT-1K dataset and evaluated on two
other open datasets, i.e., 100 challenging/pathological testing patient cases
from AbdomenCT-1K fully-supervised abdominal organ segmentation benchmark and
90 cases from TCIA+&BTCV dataset. Highly competitive or superior quantitative
segmentation results have been achieved using these datasets for four abdominal
organs of liver, kidney, spleen and pancreas with reported Dice scores improved
by 7.3% for kidneys and 9.7% for pancreas, while being ~7 times faster than two
strong baseline segmentation methods(nnUNet and CoTr).
