Deep learning segmentation for morphological assessment of optic nerve integrity in optic neuritis

Abstract

Studying the optic nerve in Multiple Sclerosis (MS) patients plays a crucial role in early diagnosis and non-invasive monitoring of disease progression, as Optic Neuritis (ON) is a frequent and often early manifestation of MS. Magnetic Resonance Imaging (MRI) is the technique of choice to assess the integrity of the optic nerve. By examining the presence of lesions in the optic nerve, clinicians can obtain insights into the progression of the disease and its impact on the central nervous system over time. The goal of this bachelor’s thesis is to generate, from MRI, detailed optic nerve profiles and morphological assessments, allowing for a comparison across different patients. These profiles can help to identify the presence of lesions, track disease progression and predict clinical outcomes. To achieve this, a cohort of subjects with MS, with and without ON, and healthy subjects will be analyzed. Deep learning models will be trained using 3D T1-weighted MRI scans from this dataset to segment the optic nerve. The assessment of optic nerve integrity will be performed by calculating the T1/T2 ratio, enabling precise detection and analysis of ON lesions and allowing for a comparison of these profiles with those of the control patients.