  Accurate segmentation of organs or lesions from medical images is crucial for
reliable diagnosis of diseases and organ morphometry. In recent years,
convolutional encoder-decoder solutions have achieved substantial progress in
the field of automatic medical image segmentation. Due to the inherent bias in
the convolution operations, prior models mainly focus on local visual cues
formed by the neighboring pixels, but fail to fully model the long-range
contextual dependencies. In this paper, we propose a novel Transformer-based
Attention Guided Network called TransAttUnet, in which the multi-level guided
attention and multi-scale skip connection are designed to jointly enhance the
performance of the semantical segmentation architecture. Inspired by
Transformer, the self-aware attention (SAA) module with Transformer Self
Attention (TSA) and Global Spatial Attention (GSA) is incorporated into
TransAttUnet to effectively learn the non-local interactions among encoder
features. Moreover, we also use additional multi-scale skip connections between
decoder blocks to aggregate the upsampled features with different semantic
scales. In this way, the representation ability of multi-scale context
information is strengthened to generate discriminative features. Benefitting
from these complementary components, the proposed TransAttUnet can effectively
alleviate the loss of fine details caused by the stacking of convolution layers
and the consecutive sampling operations, finally improving the segmentation
quality of medical images. Extensive experiments on multiple medical image
segmentation datasets from different imaging modalities demonstrate that the
proposed method consistently outperforms the state-of-the-art baselines. Our
code and pre-trained models are available at:
https://github.com/YishuLiu/TransAttUnet.
