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DFMA-ICH: a deformable mixed-attention model for intracranial hemorrhage lesion segmentation based on deep supervision

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Abstract

Intracranial hemorrhage (ICH) is a common and critical disease in clinical, with rapid progression, high disability, and mortality rates. Existing segmentation methods, such as U-Net and TransUNet, perform poorly for the problems in ICH segmentation such as small bleeding lesions, partial volume effects, and edge tissue adhesion with edema in ICH data. To solve these issues more efficiently, a deformable mixed-attention model based on deep supervision for ICH lesion segmentation (DFMA-ICH) is proposed in this study. DFMA-ICH consists of the short-term dense concatenate network (STDC) as the backbone with a mixed-attention method, an attention refining residual module (ARRM), and a mixed feature fusion module (MFFM). The mixed-attention method includes multi-scale spatial attention (MSP) and channel attention mechanism (SE) to extract rich lesion information. The double-pooling attention module (DPA) in ARRM is designed to correct features. In MFFM, different attention modules are constructed to reasonably combine low- and high-level features, and deformable convolution (DConv) is applied for boundary optimization. DFMA-ICH is trained by the deep supervision method to equalize the corresponding outputs at different stages. Overall, DFMA-ICH outperforms other advanced models on both spontaneous and traumatic ICH datasets by transfer learning with the Dice of 86.03, 80.98%, and HD of 12.35, 47.28 mm, respectively. Moreover, DFMA-ICH incurs the lowest time-space cost and exhibits the fastest inference speed. The study confirms that the proposed DFMA-ICH can provide an accurate and efficient method for ICH segmentation.

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Data and code availability

The public dataset of this study is available on the website of the 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT at: https://instance.grand-challenge.org/. In addition, when the paper is published, the corresponding code will be public at: https://github.com/1017375868/DFMA-ICH/.

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Acknowledgements

This work was supported by the Chongqing Natural Science Foundation (Grant No. CSTB2023TIAD-STX0020, CSTB2022NSCO-BHX0691, CSTB2023NSCQ-LZX0127, CSTB2022NSCQ-MSX0837), the Science and Technology Foundation of Chongqing Education Commission (Grant No. KJQN202201152), the Scientific Research Foundation of Chongqing University of Technology (Grant No. 2020zDz028), and the Graduate Innovation Project of Chongqing University of Technology (Grant No. gzlcx20232097).

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  1. Hanguang Xiao and Xinyi Shi contributed equally to this work.

Authors and Affiliations

  1. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China

    Hanguang Xiao, Xinyi Shi, Qingling Xia, Yulin Li, Li Li & Qiyuan Liu

  2. Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400030, China

    Qingling Xia & Lihua Chen

  3. Department of Radiology, The 904th Hospital of the People’s Liberation Army, Wuxi, 214041, Jiangsu, China

    Lihua Chen

  4. Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China

    Diyou Chen

  5. Institute for Traffic Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, China

    Diyou Chen & Hui Zhao

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Contributions

XS, HX, and QX took part in conceptualization. XS and HX were involved in the methodology. XS, HX, LL, YL, and QL carried out experiment data analysis. XS wrote and prepared the original draft. HX, QX, LC, DC, and HZ took part in the article review, project funding provision, and work supervision. QX, LC, DC, and HZ contributed to resources.

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Correspondence to Qingling Xia, Lihua Chen or Diyou Chen.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The studies involving human participants were reviewed and approved by Daping Hospital of Army Medical University in Chongqing, China, and Taihu Hospital in Wuxi, Jiangsu, China. The two corresponding ethics committee review documents have been submitted as attachments.

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Xiao, H., Shi, X., Xia, Q. et al. DFMA-ICH: a deformable mixed-attention model for intracranial hemorrhage lesion segmentation based on deep supervision. Neural Comput & Applic 36, 8657–8679 (2024). https://doi.org/10.1007/s00521-024-09545-w

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