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Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords

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Masse-Gignac, Nicolas, Flórez-Jiménez, Salomon, Mac-Thiong, Jean-Marc and Duong, Luc. 2023. « Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords ». Journal of Applied Clinical Medical Physics.
Compte des citations dans Scopus : 1.

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Abstract

Magnetic resonance imaging is currently the gold standard for the evaluation of spinal cord injuries.Automatic analysis of these injuries is however challenging, as MRI resolutions vary for different planes of analysis and physiological features are often distorted around these injuries. This study proposes a new CNN-based segmentation method in which information is exchanged between two networks analyzing the scans from different planes.Our aim was to develop a robust method for automatic segmentation of the spinal cord in patients having suffered traumatic injuries. The database consisted of 106 sagittal MRI scans from 94 patients with traumatic spinal cord injuries. Our method used an innovative approach where the scans were analyzed in series under the axial and sagittal plane by two different convolutional networks. The results were compared with those of Deepseg 2D from the Spinal Cord Toolbox (SCT), which was taken as state-of -the-art.Comparisons were evaluated using K-Fold crossvalidation combined with statistical t-test results on separate test data. Our method achieved significantly better results than Deepseg 2D, with an average Dice coefficient of 0.95 against 0.88 for Deepseg 2D (p < 0.001).Other metrics were also used to compare the segmentations,all of which showed significantly better results for our approach. In this study, we introduce a robust method for spinal cord segmentation which is capable of adequately segmenting spinal cords affected by traumatic injuries, improving upon the methods contained in SCT.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Duong, Luc
Affiliation: Génie logiciel et des technologies de l'information
Date Deposited: 03 Oct 2023 14:46
Last Modified: 31 Jan 2024 20:17
URI: https://espace2.etsmtl.ca/id/eprint/27888

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