An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network
Date Issued
September 2021
DOI
10.1007/978-3-030-89128-2_25
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.
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.