Open AccessAutomatic detection of multiple sclerosis lesions using Mask R‐CNN on magnetic resonance scans
Author(s) -
Süleyman Yıldırım Mehmet,
Dandıl Emre
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.1128
Subject(s) - magnetic resonance imaging , multiple sclerosis , lesion , computer science , stage (stratigraphy) , pattern recognition (psychology) , artificial intelligence , medicine , radiology , pathology , psychiatry , paleontology , biology
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..