Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

Xueqiang Zeng
,
Yingwei Guo
ORCID logo ,
Asim Zaman
ORCID logo ,
Haseeb Hassan
,
Jiaxi Lu
,
Jiaxuan Xu
,
Xiaoqiang Miao
,
Huihui Yang
,
Anbo Cao
,
Yingjian Yang
ORCID logo ,
Rongchang Chen
* ,
Yan Kang
* ORCID logo
Version 1 : Received: 25 May 2023 / Approved: 26 May 2023 / Online: 26 May 2023 (07:58:26 CEST)

A peer-reviewed article of this Preprint also exists.

Zeng, X.; Guo, Y.; Zaman, A.; Hassan, H.; Lu, J.; Xu, J.; Yang, H.; Miao, X.; Cao, A.; Yang, Y.; Chen, R.; Kang, Y. Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution. Diagnostics 2023, 13, 2161. Zeng, X.; Guo, Y.; Zaman, A.; Hassan, H.; Lu, J.; Xu, J.; Yang, H.; Miao, X.; Cao, A.; Yang, Y.; Chen, R.; Kang, Y. Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution. Diagnostics 2023, 13, 2161.

Abstract

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and patho-logical detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse at-tention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edge features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy re-duces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation tech-niques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare profes-sionals.

Keywords

cerebrovascular; airway; tubular structures; multi-scale; reverse attention; sparse convolution

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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