Open Access

ARTICLE

Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

H. M. Rehan Afzal^1,2,*, Suhuai Luo¹, Saadallah Ramadan^1,2, Jeannette Lechner-Scott^1,2,3, Mohammad Ruhul Amin³, Jiaming Li⁴, M. Kamran Afzal⁵

1 School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia
2 Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
3 Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW 2305, Australia
4 CSIRO Data61, Marsfield, NSW 2122, Australia
5 School of Computer Science and Technology, Xiamen University, Xiamen, 361005, China

* Corresponding Author: H. M. Rehan Afzal. Email:

(This article belongs to the Special Issue: Deep Learning Trends in Intelligent Systems)

Computers, Materials & Continua 2021, 66(1), 977-991. https://doi.org/10.32604/cmc.2020.012448

Received 01 July 2020; Accepted 24 July 2020; Issue published 30 October 2020

Abstract

The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is implemented to segment lesions accurately, and the second network aims to reduce the false positives to increase efficiency. The system consists of two parallel convolutional pathways, where one pathway is concatenated to the second and at the end, the fully connected layer is replaced with CNN. Three routine MRI sequences T1-w, T2-w and FLAIR are used as input to the CNN, where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w & T2-w are used to reduce MRI artifacts. We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI. Quantitative and qualitative evaluation has been performed with various metrics like false positive rate (FPR), true positive rate (TPR) and dice similarities, and were compared to current state-of-the-art methods. The proposed method shows consistent higher precision and sensitivity than other methods. The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners, with a precision up to 90%.

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