Automatic quantification of osteoarthritis features in MRI using deep learning methods
| dc.contributor.advisor | Ronsky, Janet L. | |
| dc.contributor.author | Felfeliyan, Banafshe | |
| dc.contributor.committeemember | Jaremko, Jacob L. | |
| dc.contributor.committeemember | Lebel, Marc R. | |
| dc.contributor.committeemember | Li, Matthew D. | |
| dc.contributor.committeemember | Wong, Andy K. | |
| dc.contributor.committeemember | Far, Behrouz | |
| dc.contributor.committeemember | Forkert, Nils D. | |
| dc.date | 2023-06 | |
| dc.date.accessioned | 2023-03-22T17:13:40Z | |
| dc.date.available | 2023-03-22T17:13:40Z | |
| dc.date.issued | 2023-03-20 | |
| dc.description.abstract | Osteoarthritis (OA) is a progressive irreversible disease that affects the whole joint involving various tissues including cartilage, bone, and synovium. Accurate OA diagnosis and management requires a thorough understanding of its pathogenesis, along with precise monitoring of its trajectories and changes associated with treatment. Magnetic resonance imaging (MRI) is an excellent tool for understanding OA progression and diagnosis. However, its assessment relies on reader-dependent semiquantitative scores, which are subjective, laborious, and insensitive to small changes. An automated OA assessment pipeline using deep learning (DL) can overcome limitations but faces challenges, including low generalizability, label scarcity, and noisy clinical labels with high variability. Therefore, this thesis’s objective is to overcome challenges associated with automated OA assessment by developing DL methods for the quantitative assessment of predominant OA biomarkers (cartilage, effusion-synovitis, and bone marrow lesion (BML)). This thesis proposed ImprovedMaskRCNN (iMaskRCNN), an accurate DL algorithm for multiscale segmentation of tissues involved in OA (femur, tibia, femoral and tibial cartilages, and hip effusion), which improves Dice score by 1-7%. Algorithm outputs are used to quantify cartilage thickness and hip effusion volume. An unsupervised domain adaptation pipeline using iMaskRCNN and CycleGAN was proposed to overcome DL’s low generalizability across MRI sequences. Therefore, a weakly supervised training strategy was proposed for BML segmentation, using soft labels obtained from clinical scoring and a noise-robust loss, which increased the Dice score and recall by 8% and 22%, respectively. Effusion segmentation was achieved using a self-supervised learning approach to pretrain the iMaskRCNN algorithm for training with a few-labeled dataset and improved the Dice by more than 8%. Following validation of the developed methods, results were compared with patient outcomes indicating strong associations of the extracted BML and effusion features (Δpain vs. ΔDL-effusion r =0.59, and Δpain vs. Δtibial BML r=0.49). This research provides an efficient integrated tool for quantifying OA features from MRI to accurately monitor OA progression and structural changes. This tool enables large population analyses and the extraction of more sensitive measures for clinicians to understand OA. The proposed methods have strong potential to apply to other joints, tissues, and medical image analysis problems. | en_US |
| dc.identifier.citation | Felfeliyan, B. (2023). Automatic quantification of osteoarthritis features in MRI using deep learning methods (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1880/115953 | |
| dc.identifier.uri | https://dx.doi.org/10.11575/PRISM/40802 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | MRI | en_US |
| dc.subject | Deep-Learning | en_US |
| dc.subject | Image segmentation | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Medical Imaging | en_US |
| dc.subject | Self Supervised Learning | en_US |
| dc.subject | Weakly Supervised Learning | en_US |
| dc.subject | Domain Adaptation | en_US |
| dc.subject | Bone | en_US |
| dc.subject | Cartilage | en_US |
| dc.subject | Bone marrow lesion | en_US |
| dc.subject | Effusion-synovitis | en_US |
| dc.subject | Osteoarthritis | en_US |
| dc.subject.classification | Education--Health | en_US |
| dc.subject.classification | Radiology | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.subject.classification | Engineering--Biomedical | en_US |
| dc.title | Automatic quantification of osteoarthritis features in MRI using deep learning methods | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Biomedical | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | false | en_US |