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Abstract #0022

Automatic segmentation and follow-up of optic pathway gliomas using deep learning and based on conventional MRI

Moran Artzi1,2,3, Sapir Gershov4, Idan Bressler1,4, Liat Ben-Sira2,5,6, Shlomi Constantini6,7, Tomer Gazit1, Tali Halag-Milo1, and Dafna Ben Bashat1,2,3

1Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel - Aviv, Israel, 2Sackler Faculty of Medicine, Tel Aviv University, Tel - Aviv, Israel, 3Sagol School of Neuroscience, Tel Aviv University, Tel - Aviv, Israel, 4The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel - Aviv, Israel, 5Division of Radiology, Tel Aviv Sourasky Medical Center, Tel - Aviv, Israel, 6The Gilbert Israeli Neurofibromatosis Center, Tel - Aviv, Israel, 7Department of Pediatric Neurosurgery, Tel - Aviv, Israel

Optic pathway gliomas (OPG) are heterogeneous tumors with complex shape. The aim of this study was to implement a deep-learning approach for automatic segmentation and follow-up of patients with OPG based on conventional MRI. A total of 354 MRI scans from 53 patients where included. A neural-network with a U-net architecture was trained for segmentation of lesion area. The similarity coefficient score between segmentation results and ground truth was 0.812±0.159, with sensitivity=0.799±0.188, specificity=0.999±0.002 and correlation of r=0.987 (p<0.001) between lesion volumes. These results demonstrate the potential applicability of the proposed method for automatic radiological follow-up of patients with OPG.

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