  When delineating lesions from medical images, a human expert can always keep
in mind the anatomical structure behind the voxels. However, although
high-quality (though not perfect) anatomical information can be retrieved from
computed tomography (CT) scans with modern deep learning algorithms, it is
still an open problem how these automatically generated organ masks can assist
in addressing challenging lesion segmentation tasks, such as the segmentation
of colorectal cancer (CRC). In this paper, we develop a novel Anatomy-Guided
segmentation framework to exploit the auto-generated organ masks to aid CRC
segmentation from CT, namely AG-CRC. First, we obtain multi-organ segmentation
(MOS) masks with existing MOS models (e.g., TotalSegmentor) and further derive
a more robust organ of interest (OOI) mask that may cover most of the
colon-rectum and CRC voxels. Then, we propose an anatomy-guided training patch
sampling strategy by optimizing a heuristic gain function that considers both
the proximity of important regions (e.g., the tumor or organs of interest) and
sample diversity. Third, we design a novel self-supervised learning scheme
inspired by the topology of tubular organs like the colon to boost the model
performance further. Finally, we employ a masked loss scheme to guide the model
to focus solely on the essential learning region. We extensively evaluate the
proposed method on two CRC segmentation datasets, where substantial performance
improvement (5% to 9% in Dice) is achieved over current state-of-the-art
medical image segmentation models, and the ablation studies further evidence
the efficacy of every proposed component.
