  Non-contrast computed tomography (NCCT) is commonly acquired for lung cancer
screening, assessment of general abdominal pain or suspected renal stones,
trauma evaluation, and many other indications. However, the absence of contrast
limits distinguishing organ in-between boundaries. In this paper, we propose a
novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT)
context to compute non-contrast segmentation without ground-truth label. Unlike
generative adversarial approaches, we compute the pairwise morphological
context with CECT to provide teacher guidance instead of generating fake
anatomical context. Additionally, we further augment the intensity correlations
in 'organ-specific' settings and increase the sensitivity to organ-aware
boundary. We validate our approach on multi-organ segmentation with paired
non-contrast & contrast-enhanced CT scans using five-fold cross-validation.
Full external validations are performed on an independent non-contrast cohort
for aorta segmentation. Compared with current abdominal organs segmentation
state-of-the-art in fully supervised setting, our proposed pipeline achieves a
significantly higher Dice by 3.98% (internal multi-organ annotated), and 8.00%
(external aorta annotated) for abdominal organs segmentation. The code and
pretrained models are publicly available at
https://github.com/MASILab/ContrastMix.
