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Fully automatic identification of post-treatment infarct lesions after endovascular therapy based on non-contrast computed tomography

  • S.i.: Deep Learning in Multimodal Medical Imaging for Cancer Detection
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Abstract

Non-contrast computed tomography (NCCT) of the brain is critical to patients with acute ischemic stroke who receive thrombolysis and thrombectomy. It can help identify reperfusion-related hemorrhage, edema which need intervention. It also can guide the timing and intensity of antithrombotic therapy. Rapid, accurate, and automated detection and segmentation of acute ischemic lesions after endovascular therapy (EVT) are highly needed. In this work, we propose a novel encoder-decoder network for fully automatic segmentation of acute ischemic lesions after EVT on NCCT, which is named ISCT-EDN. NCCT images of AIS (acute ischemic stroke) patients who underwent EVT in a multicenter cohort study were collected in this study. ISCT-EDN takes hierarchical network as backbone. Feature pyramid network (FPN) is designed to aggregate features from multi stages of backbone. Reasonable feature fusion strategy is considered in FPN to enhance multi-level propagation. In addition, to overcome the limitation of fixed geometric structure of convolution for multi-range dependency exploitation, non-local parallel decoder is introduced with deformable convolution and self-attention. The proposed model was compared with 7 segmentation models which are commonly used in the medical domain and the performance was superior to other models in in the segmentation of post-treatment infarct lesions on NCCT images of AIS patients after EVT.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Ximing Nie and Xiran Liu are co-first authors. The authors thank Yanyan Ma, Xiaoyu Cui, Lei Yu and other study quality control coordinators for their meticulous work on data quality control, and are grateful for the participation and engagement of all the subjects and investigators of the RESCUE-RE trial.

Funding

This work was supported by the National Key R&D program of China (2016YFC1307301), National Natural Science Foundation of China (82001920) and Beijing Municipal Administration of Hospitals’ Youth Programme (QML20210503), National Nature Science and Foundation of China (62202044), Guangdong Basic and Applied Basic Research Foundation (2020A1515110431), Scientific and Technological Innovation Foundation of Foshan (BK22BF009).

Author information

Author notes

  1. Ximing Nie and Xiran Liu have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 119 West Road, South 4th Ring, Fengtai District, Beijing, 100070, China

    Ximing Nie, Xiran Liu, Yufei Wei, Qixuan Lu, Jiaping Chen, Tianhang Liu, Hongyi Yan, Zhonghua Yang, Miao Wen, Yuesong Pan & Liping Liu

  2. China National Clinical Research Center for Neurological Diseases, Beijing, China

    Ximing Nie, Xiran Liu, Yufei Wei, Qixuan Lu, Jiaping Chen, Tianhang Liu, Hongyi Yan, Zhonghua Yang, Miao Wen, Yuesong Pan & Liping Liu

  3. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Hao Yang, Chao Huang & Long Wang

  4. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Feng Shi

  5. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Weibin Gu & Xinyi Hou

  6. Department of Neurology, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang, China

    Haiwei Bai

  7. Shunde Innovation School, University of Science and Technology Beijing, Foshan, China

    Long Wang

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  1. Ximing Nie
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  2. Xiran Liu
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  3. Hao Yang
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  16. Chao Huang
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  17. Long Wang
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  18. Liping Liu
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Contributions

XN: Conceptualization, Writing—Review and Editing, Project administration XL: Conceptualization, Writing—Original Draft, Project administration HY: Methodology, Software, Formal analysis FS: Validation WG: Investigation, Formal analysis XH: Investigation, Formal analysis YW: Investigation, Data Curation QL: Investigation, Data Curation HB: Investigation JC: Investigation TL: Investigation HY: Formal analysis ZY: Supervision MW: Supervision YP: Formal analysis CH: Methodology, Supervision LW: Methodology, Supervision LL: Conceptualization, Writing—Review and Editing, Resources, Project administration.

Corresponding authors

Correspondence to Chao Huang or Liping Liu.

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Nie, X., Liu, X., Yang, H. et al. Fully automatic identification of post-treatment infarct lesions after endovascular therapy based on non-contrast computed tomography. Neural Comput & Applic 35, 22101–22114 (2023). https://doi.org/10.1007/s00521-022-08094-4

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