Open AccessFully automated segmentation of wrist bones on T2-weighted fat-suppressed MR images in early rheumatoid arthritis
Author(s) -
Lun M. Wong,
Lin Shi,
Xiaoxi Fan,
James F. Griffith
Publication year - 2019
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims.2019.04.03
Subject(s) - wrist , segmentation , medicine , convolutional neural network , magnetic resonance imaging , sørensen–dice coefficient , rheumatoid arthritis , artificial intelligence , computer science , pattern recognition (psychology) , image segmentation , radiology
Magnetic resonance imaging (MRI) allows accurate determination of soft tissue and bone inflammation in rheumatoid arthritis. Inflammation can be measured semi-quantitatively using the well-established RA-MRI scoring system (RAMRIS), but its application is time consuming in routine clinical practice. To fully realize an automated quantitation of inflammation scoring for clinical use, automatic segmentation of the wrist bones on MR imaging is needed. Most previous studies extracted the wrist bones on T1-weighted (T1W) MR images, and then used registration to segment T2W fat-suppressed images for bone marrow oedema quantification, introducing spatial errors into the process. Relatively little work has tried segmentation directly from T2W fat-suppressed images and no prior study have used convolution neural network (CNN) to segment the wrist bones. The purpose of this study is to develop a CNN-based algorithm for automated segmentation of the wrist bones in early rheumatoid arthritis (ERA) patients on T2W fat-saturated MR images.