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

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 bi...

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Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 8; no. 1; pp. 55 - 68
Main Authors:	Chen, Bingzhi, Liu, Yishu, Zhang, Zheng, Lu, Guangming, Kong, Adams Wai Kin
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Biomedical image processing Computer architecture Convolution Convolutional neural networks Decoding Encoders-Decoders Image quality Image segmentation Medical diagnostic imaging Medical image segmentation Medical imaging multi-level guided attention multi-scale skip connection Semantics transformer Transformers
ISSN:	2471-285X, 2471-285X
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Abstract	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.
AbstractList	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.
Author	Liu, Yishu Zhang, Zheng Lu, Guangming Chen, Bingzhi Kong, Adams Wai Kin
Author_xml	– sequence: 1 givenname: Bingzhi orcidid: 0000-0002-2497-6214 surname: Chen fullname: Chen, Bingzhi email: chenbingzhi@m.scnu.edu.cn organization: School of Software, South China Normal University, Foshan, Guangdong, China – sequence: 2 givenname: Yishu orcidid: 0000-0003-0465-5381 surname: Liu fullname: Liu, Yishu email: liuyishu@stu.hit.edu.cn organization: Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, China – sequence: 3 givenname: Zheng orcidid: 0000-0003-1470-6998 surname: Zhang fullname: Zhang, Zheng email: zhengzhang@hit.edu.cn organization: Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, China – sequence: 4 givenname: Guangming orcidid: 0000-0003-1578-2634 surname: Lu fullname: Lu, Guangming email: luguangm@hit.edu.cn organization: Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, China – sequence: 5 givenname: Adams Wai Kin orcidid: 0000-0002-9728-9511 surname: Kong fullname: Kong, Adams Wai Kin email: adamskon@ntu.edu.sg organization: School of Computer Science and Engineering, Nanyang Technological University, Singapore
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Snippet	Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years,...
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SubjectTerms	Biomedical image processing Computer architecture Convolution Convolutional neural networks Decoding Encoders-Decoders Image quality Image segmentation Medical diagnostic imaging Medical image segmentation Medical imaging multi-level guided attention multi-scale skip connection Semantics transformer Transformers
Title	TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation
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