Abstract
Accurate segmentation of rectal cancer and rectal wall based on
high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the
basis of rectal cancer staging. However, complex imaging background,
highly characteristics variation and poor contrast hindered the research
progress of the automatic rectal cancer segmentation. In this study, a
multi-task learning network, namely mask segmentation with boundary
constraints (MSBC-Net), is proposed to overcome these limitations and to
obtain accurate segmentation results by locating and segmenting rectal
cancer and rectal wall automatically. Specifically, at first, a region
of interest (RoI)-based segmentation strategy is designed to enable
end-to-end multi-task training, where a sparse object detection module
is used to automatically localize and classify rectal cancer and rectal
wall to mitigate the problem of background interference, and a mask and
boundary segmentation block is used to finely segment the RoIs; second,
a modulated deformable backbone is introduced to handle the variable
features of rectal cancer, which effectively improves the detection
performance of small objects and adaptability of the proposed model.
Moreover, the boundary head is fused into the mask head to segment the
ambiguous boundary of the target and constrain the mask head to obtain
more refined segmentation results. In total, 592 annotated rectal cancer
patients in MRI-HRT2 are enrolled, and the comprehensive results show
that the proposed MSBC-Net outperforms state-of-the-art methods with a
dice similarity coefficient (DSC) of 0.801 (95\% CI,
0.791-0.811), which can be well extended to other medical image
segmentation tasks with high potential clinical applicability.