Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks

• Published in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 2045 - 2048
• Main Authors: Oliveira, Marcela de, Santinelli, Felipe Balistieri, Piacenti-Silva, Marina, Rocha, Fernando Coronetti Gomes, Barbieri, Fabio Augusto, Lisboa-Filho, Paulo Noronha, Santos, Jorge Manuel, Cardoso, Jaime dos Santos
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.12.2020
• Subjects: brain lesions quantification; Brain modeling; CNN; Diseases; Filter banks; Image segmentation; Lesions; Magnetic resonance imaging; MRI; multiple sclerosis; Training
• DOI: 10.1109/BIBM49941.2020.9313244

Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm 3 . We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS.