Segmentation of white matter lesions in multicentre FLAIR MRI

• Main Authors: April Khademi, Adam Gibicar, Giordano Arezza, Justin DiGregorio, Pascal N. Tyrrell, Alan R. Moody
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Neuroimage: Reports
• Subjects: FLAIR MRI; White matter lesions (WML); Segmentation; Deep learning
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666956021000428

White matter lesions (WML) in the brain are thought to be related to ischemic processes, demyelination, and axonal degeneration. The presence of WML predict cognitive decline, dementia, stroke, and death. Lesion progression increases these risks, making WML significant clinical biomarkers for investigation. To analyze WML objectively, consistently, and efficiently, automated WML segmentation methods for neurological MRI have been the focus of extensive research efforts. There have been many unsupervised and traditional machine learning methods proposed over the years. Recently, deep learning architectures have been utilized for WML segmentation with promising results. In this work, we evaluate seven WML segmentation tools for multicentre fluid attenuated inversion recovery (FLAIR) MRI. Two traditional methods were evaluated, one unsupervised method and the other a traditional machine learning approach. The traditional methods were compared to five deep learning-based approaches. FLAIR MRI have the advantage of highlighting WML lesions robustly and are used routinely in neurological workflows. Automated WML segmentation tools for FLAIR MRI could optimize clinical workflows and improve patient care. The WML segmentation algorithms were evaluated on a multicentre, multi-disease FLAIR MRI database acquired with varying scanners and protocols. In total 252 imaging volumes (~13 K image slices) with annotations, from 5 multicentre datasets (33 imaging centres) were used to train, validate and test the WML segmentation methods. Two clinical datasets, which include dementia and vascular disease pathologies, and three open-source datasets were used. To examine clinical utility of each algorithm and establish proof of effectiveness, algorithms were evaluated over several dimensions related to accuracy, generalizability, and robustness to pathology. This work presents a framework for evaluating the efficacy of WML segmentation algorithms for improved reliability, patient safety and clinical trials. Of all methods, SC U-Net was found to be the best algorithm for WML segmentation in terms of highest Dice similarity coefficient (DSC) over most dimensions (mean DSC = 0.71 over all volumes). Deep learning methods outperformed traditional methods, especially in lower lesion loads, but were not able to generalize across all disease categories or datasets.