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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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.
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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).
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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.
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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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DOI: https://doi.org/10.1007/s00521-022-08094-4