TY  - JOUR
AU  - P. Canals
AU  - Simone Balocco
AU  - O. Diaz
AU  - J. Li
AU  - A. Garcia Tornel
AU  - M. Olive Gadea
AU  - M. Ribo
PY  - 2023//
TI  - A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning
T2  - CMIG
JO  - Computerized Medical Imaging and Graphics
VL  - 104
IS  - 102170
KW  - Artificial intelligence
KW  - Deep learning
KW  - Stroke
KW  - Thrombectomy
KW  - Vascular feature extraction
KW  - Vascular tortuosity
N2  - 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.
UR  - https://www.sciencedirect.com/science/article/pii/S0895611122001409
UR  - http://dx.doi.org/CBD2023
N1  - MILAB
ID  - P. Canals2023
ER  -