Brain Lesion Segmentation Using Deep Learning and Its Role in Computer-Aided Differential Diagnosis of Multiple Sclerosis and Neuromyelitis Optica

• Published in: IEEE ACCESS
• Main Authors: Memon, Khuhed; Yahya, Norashikin; Siddiqui, Shahabuddin; Hashim, Hilwati; Remli, Rabani; Mustapha, Aida-Widure Mustapha Mohd; Yusoff, Mohd Zuki; Ali, Syed Saad Azhar
• Format: Article
• Language: English
• Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2024
• Subjects: Computer Science; Engineering; Telecommunications
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001349763800001

Neurological disorders are debilitating diseases and cause significant morbidity worldwide, with some resulting in mortality. Magnetic Resonance Imaging (MRI) is the prime modality to evaluate most of the diseases involving the brain. The organic diseases of the brain show lesions which are well-appreciated on MRI, but require radiologists and medical experts for delineation. This is crucial for the differential diagnosis of diseases producing similar plaque patterns on the brain. Artificial Intelligence (AI) techniques can help in automatic brain lesion segmentation using massive publicly available data to train Computer-Aided Differential Diagnosis (CADD) algorithms. The accuracy of such CADD algorithms hugely relies on the accuracy of lesion segmentation Deep Learning (DL) models. In this research, DeepLabV3+ architecture is used for semantic segmentation of brain lesions using multiple publicly available datasets. In order to enhance the accuracy, additional ground truth (GT) lesion masks from MICCAI-21 dataset were obtained from a consultant radiologist, and used for training and testing. In addition, the developed algorithm underwent testing using 5 Multiple Sclerosis (MS) and 5 Neuromyelitis Optica (NMO) cases obtained from UiTM Hospital, and 35 MS and 27 NMO cases from HCTM Malaysia, and annotated by radiologists. The Dice score of the trained DL model on test data from MICCAI-21, MICCAI-16, Baghdad Teaching Hospital dataset, HCTM, and UiTM data is 0.7304, 0.6426, 0.4117, 0.5308, and 0.4951, respectively. The model is embedded in an app called NeuroImaging Lesion Extractor (NILE) and is available for public use. This app can serve as an assistive tool for experts in developing differential diagnosis algorithms for demyelinating diseases like MS and NMO.