TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation

By Bingzhi Chen, Yishu Liu, Zheng Zhang, Guangming Lu, Adams Wai-Kin Kong. IEEE Transactions on Emerging Topics in Computational Intelligence, 2021

411 citations recorded by the source corpus.

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 article, 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 were conducted on multiple medical image segmentation datasets from various imaging modalities, which demonstrate that the proposed method consistently outperforms the existing state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/69b985ae4a8331107cdfa148335bf08d15787bda

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Citations in the corpus

Medical Image Segmentation Review: The Success of U-Net (2022, result): “There are still other studies such as [153], [154] noteworthy to review, but due to the limitation of the paper, we excluded them.”
Transformers in Medical Imaging: A Survey (2022, background): “The first work in this category is TransAttUNet [169] that leverages guided attention and multi-scale skip connection to enhance the flexibility of traditional UNet.”
Transformers in Medical Image Analysis: A Review (2022, background): “gland segmentation [24], [84], [101], [91], nucleus segmentation [24], [84], [91], [101], cell segmentation [87], [105], [106], spleen segmentation [90], lung field/COVID-19 pneumonia”
nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer (2021, background): “and most of its followers [13], [14], [15], [16] is to treat convnets as main bodies, on top of which transformers are further applied to capture long-term dependencies.”