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Author (up) P. Canals; Simone Balocco; O. Diaz; J. Li; A. Garcia Tornel; M. Olive Gadea; M. Ribo edit  url
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  Title A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning Type Journal Article
  Year 2023 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 104 Issue 102170 Pages  
  Keywords Artificial intelligence; Deep learning; Stroke; Thrombectomy; Vascular feature extraction; Vascular tortuosity  
  Abstract Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.  
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  Notes MILAB Approved no  
  Call Number Admin @ si @ CBD2023 Serial 4005  
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