An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network

Abstract

This work proposes and evaluates a semi-automated integrated segmentation system for multiple sclerosis (MS) lesions in fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance images (MRI). The proposed system uses an adaptive two-dimensional (2D) full convolutional neural network (CNN) and is applied to each MRI brain slice separately. The system is based on a U-Net architecture and allows manual error corrections by the user. This task produces continuing additional improvements to the accuracy of the segmentation system, which can be adapted and reconfigured interactively based on the data entered by the user of the system. The system was evaluated based on the ISBI dataset, on 20 MRI brain images acquired from 5 MS subjects who repeated their examinations in four consecutive time points (TP1-TP4). Manual lesion delineations were provided by two different experts. A Dice Similarity Coefficient (DSC) of 0.76 was achieved using the proposed system which is the highest achieved also by another system. A higher DSC of 0.82 was achieved when the proposed system was evaluated on TP4 images only. A larger dataset will be analyzed in the future, and new measurement metrics will be suggested.