Accurate segmentation of medical images is crucial for diagnostic purposes,
including cell segmentation, tumor identification, and organ localization.
Traditional convolutional neural network (CNN)-based approaches struggled to
achieve precise segmentation results due to their limited receptive fields,
particularly in cases involving multi-organ segmentation with varying shapes
and sizes. The transformer-based approaches address this limitation by
leveraging the global receptive field, but they often face challenges in
capturing local information required for pixel-precise segmentation. In this
work, we introduce DwinFormer, a hierarchical encoder-decoder architecture for
medical image segmentation comprising a directional window (Dwin) attention and
global self-attention (GSA) for feature encoding. The focus of our design is
the introduction of Dwin block within DwinFormer that effectively captures
local and global information along the horizontal, vertical, and depthwise
directions of the input feature map by separately performing attention in each
of these directional volumes. To this end, our Dwin block introduces a nested
Dwin attention (NDA) that progressively increases the receptive field in
horizontal, vertical, and depthwise directions and a convolutional Dwin
attention (CDA) that captures local contextual information for the attention
computation. While the proposed Dwin block captures local and global
dependencies at the first two high-resolution stages of DwinFormer, the GSA
block encodes global dependencies at the last two lower-resolution stages.
Experiments over the challenging 3D Synapse Multi-organ dataset and Cell HMS
dataset demonstrate the benefits of our DwinFormer over the state-of-the-art
approaches. Our source code will be publicly available at
https://github.com/Daniyanaj/DWINFORMER.

DwinFormer 是一个层级编码器解码器架构，具有方向窗口（Dwin）注意力和全局自注意力（GSA）用于特征编码，通过在水平、垂直和深度方向分别执行注意力，在输入特征图的这些方向体积中有效地捕捉局部和全局信息，实验证明它在医学图像分割方面优于当前最先进的方法。