Integrating dual attention with TransUNet for esophagus image segmentation

Zhehao Yu; Shaochen Jiang; Liejun Wang

doi:10.1117/12.3033544

13 June 2024 Integrating dual attention with TransUNet for esophagus image segmentation

Zhehao Yu, Shaochen Jiang, Liejun Wang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801F (2024) https://doi.org/10.1117/12.3033544
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Esophageal cancer is a common digestive tract tumor with a high mortality rate. Currently, the identification of esophageal carcinoma predominantly hinges on thoracic computed tomography scanning. Because the area of the esophagus on CT images is small and belongs to the small target region, the traditional image segmentation network is difficult to accurately segment the tumor area of esophageal cancer. Although the traditional U-Net architecture and Transformer integrated variants perform well in medical segmentation tasks, they lack image localization and channel feature extraction capabilities. To address the aforementioned issues, this manuscript proposes a novel medical image segmentation model, christened DAA-TransUNet. We propose a new module called Dual Attention Area (DAA) module,it can extract image position and channel features. And we combine the DAA module and the Efficient Channel Attention (ECA) module into a new module, which we call the Efficient Channel Dual Attention (ECDA) module. DAA-TransUNet integrates Transformer and ECDA modules into a traditional U-shaped architecture, enabling it to extract not only global and local information, but also image position and channel features. We have verified the superior performance of our network by performing numerous experiments on our collected esophageal dataset. We used DAA-TransUNet to perform experiments on our collected dataset, and the average DSC(%) reached 75.26% and the average HD95(mm) reached 3.54.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhehao Yu, Shaochen Jiang, and Liejun Wang "Integrating dual attention with TransUNet for esophagus image segmentation", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801F (13 June 2024); https://doi.org/10.1117/12.3033544

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Feature extraction

Transformers

Cancer

Content addressable memory

Convolution

Computed tomography

Show All Keywords

Keywords/Phrases

Search In:

Publication Years