  Automatic segmentation of multiple organs and tumors from 3D medical images
such as magnetic resonance imaging (MRI) and computed tomography (CT) scans
using deep learning methods can aid in diagnosing and treating cancer. However,
organs often overlap and are complexly connected, characterized by extensive
anatomical variation and low contrast. In addition, the diversity of tumor
shape, location, and appearance, coupled with the dominance of background
voxels, makes accurate 3D medical image segmentation difficult. In this paper,
a novel large-kernel (LK) attention module is proposed to address these
problems to achieve accurate multi-organ segmentation and tumor segmentation.
The advantages of convolution and self-attention are combined in the proposed
LK attention module, including local contextual information, long-range
dependence, and channel adaptation. The module also decomposes the LK
convolution to optimize the computational cost and can be easily incorporated
into FCNs such as U-Net. Comprehensive ablation experiments demonstrated the
feasibility of convolutional decomposition and explored the most efficient and
effective network design. Among them, the best Mid-type LK attention-based
U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving
state-of-the-art segmentation performance. The performance improvement due to
the proposed LK attention module was also statistically validated.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning