BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image
  Segmentation
Omid Nejati Manzari Javad Mirzapour Kaleybar Hooman Saadat Shahin Maleki
The accurate segmentation of medical images is critical for various
healthcare applications. Convolutional neural networks (CNNs), especially Fully
Convolutional Networks (FCNs) like U-Net, have shown remarkable success in
medical image segmentation tasks. However, they have limitations in capturing
global context and long-range relations, especially for objects with
significant variations in shape, scale, and texture. While transformers have
achieved state-of-the-art results in natural language processing and image
recognition, they face challenges in medical image segmentation due to image
locality and translational invariance issues. To address these challenges, this
paper proposes an innovative U-shaped network called BEFUnet, which enhances
the fusion of body and edge information for precise medical image segmentation.
The BEFUnet comprises three main modules, including a novel Local
Cross-Attention Feature (LCAF) fusion module, a novel Double-Level Fusion (DLF)
module, and dual-branch encoder. The dual-branch encoder consists of an edge
encoder and a body encoder. The edge encoder employs PDC blocks for effective
edge information extraction, while the body encoder uses the Swin Transformer
to capture semantic information with global attention. The LCAF module
efficiently fuses edge and body features by selectively performing local
cross-attention on features that are spatially close between the two
modalities. This local approach significantly reduces computational complexity
compared to global cross-attention while ensuring accurate feature matching.
BEFUnet demonstrates superior performance over existing methods across various
evaluation metrics on medical image segmentation datasets.