Automatic detection of multiple sclerosis lesions using Mask R‐CNN on magnetic resonance scans. Issue 16 (23rd February 2021)

• Record Type: Journal Article
• Authors: Süleyman Yıldırım, Mehmet; Dandıl, Emre
• Is Part Of: IET image processing. Volume 14:Issue 16(2020)
• Page Start: 4277
• Page End: 4290
• Publication Date: 2021-02-23
• Subjects: biomedical MRI -- bioelectric potentials -- patient diagnosis -- neurophysiology -- image segmentation -- visual evoked potentials -- neural nets -- medical image processing -- diseases -- brain; automatic detection -- multiple sclerosis lesions -- Mask R‐CNN -- magnetic resonance scans -- central nervous system -- inflammation surrounding nerve cells -- progressive MS attacks -- clinical findings -- magnetic resonance imaging findings -- MRI -- MS detection -- Mask regional convolutional neural network based method -- MS lesions -- interest detection stage -- region proposal network -- different lesion sizes -- 87.03% precision rates -- volumetric overlap error; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.1128
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Multiple Sclerosis (MS) causes the central nervous system to malfunction due to inflammation surrounding nerve cells. Detection of MS at an early stage is very important to prevent progressive MS attacks. Clinical findings, cerebrospinal fluid examinations, the evoked potentials, magnetic resonance imaging (MRI) findings have an important role in the diagnosis and follow‐up of MS. However, many of the findings on MRI may indicate brain disorders other than MS. In addition, the clinical practices accepted by physicians for MS detection are very limited. In this study, a Mask R‐CNN based method in two dataset is proposed for the automatic detection of MS lesions on magnetic resonance scans. We also improved the ROI detection stage with RPN in the Mask R‐CNN to easily adapt for different lesion sizes. MS lesions in different sizes in the dataset are successfully detected with 84.90% Dice similarity rate and 87.03% precision rates using the proposed method. In addition, volumetric overlap error and lesion‐wise true positive rate are obtained as 12.97% and 73.75%, respectively. Moreover, performance tests of the use of different numbers of GPU hardware structures are also performed and the evaluation of its effects on processing speed is performed on experimental studies..