Detecting, characterizing and visualizing multiple sclerosis in optical coherence tomography through biomarker selection

Multiple sclerosis (MS) is a chronic neurodegenerative disease affecting the central nervous system, which is the primary cause of non-traumatic neurological disability among young adults. Diagnosing MS is challenging yet crucial for effective patient treatment. Optical Coherence Tomography (OCT) has emerged as a non-invasive and efficient tool for analysing optic nerv alterations and assessing neurodegeneration in MS, particularly through changes in retinal thickness of layers such as the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL). In this work, we investigate the potential of using changes across all retinal layer thickness as a biomarker for MS detection. To accomplish this, we propose a fully automatic system consisting of an MS classification model and a pathological thickness visualization model. First, our system employs a fully convolutional neural network to segment retinal layers and choroidal vessels and to calculate the extraction of layer thickness at varying granularities. Global and local layer thickness are used as inputs for the MS classification model. A genetic multi-objective algorithm is used for effective feature selection. Concurrently, voxel-level layer thickness serves as input for the visualization model that generates a 2D probability map where the pathological regions are highlighted. This map contributes to an interactive 3D reconstruction that provides a swift overview of MS-associated thickness changes. Extensive experimentation on real clinical MS cases validates the significant potential of the proposed system for practical clinical applications and showcases its efficacy in enhancing diagnostic precision and efficiency.