  Automated abdominal multi-organ segmentation is a crucial yet challenging
task in the computer-aided diagnosis of abdominal organ-related diseases.
Although numerous deep learning models have achieved remarkable success in many
medical image segmentation tasks, accurate segmentation of abdominal organs
remains challenging, due to the varying sizes of abdominal organs and the
ambiguous boundaries among them. In this paper, we propose a boundary-aware
network (BA-Net) to segment abdominal organs on CT scans and MRI scans. This
model contains a shared encoder, a boundary decoder, and a segmentation
decoder. The multi-scale deep supervision strategy is adopted on both decoders,
which can alleviate the issues caused by variable organ sizes. The boundary
probability maps produced by the boundary decoder at each scale are used as
attention to enhance the segmentation feature maps. We evaluated the BA-Net on
the Abdominal Multi-Organ Segmentation (AMOS) Challenge dataset and achieved an
average Dice score of 89.29$\%$ for multi-organ segmentation on CT scans and an
average Dice score of 71.92$\%$ on MRI scans. The results demonstrate that
BA-Net is superior to nnUNet on both segmentation tasks.
