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Automatic segmentation, classification, and follow‐up of optic pathway gliomas using deep learning and fuzzy c‐means clustering based on MRI
Author(s) -
Artzi Moran,
Gershov Sapir,
BenSira Liat,
Roth Jonathan,
Kozyrev Danil,
Shofty Ben,
Gazit Tomer,
HalagMilo Tali,
Constantini Shlomi,
Ben Bashat Dafna
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14489
Subject(s) - segmentation , artificial intelligence , computer science , voxel , magnetic resonance imaging , deep learning , pattern recognition (psychology) , cluster analysis , radiology , medicine
Purpose Optic pathway gliomas (OPG) are low‐grade pilocytic astrocytomas accounting for 3‐5% of pediatric intracranial tumors. Accurate and quantitative follow‐up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. Methods A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T 2 and post‐contrast T 1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro‐radiologist, and inter‐ and intra‐rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep‐learning method with U‐Net+ResNet architecture. A fivefold cross‐validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel‐based classification of the tumor into enhanced, non‐enhanced, and cystic components was performed using fuzzy c‐means clustering. Results The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel‐based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non‐enhanced, enhanced, and cystic components, respectively. Conclusion This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.