Automated segmentation of the healed anterior cruciate ligament from T2* relaxometry MRI scans. Issue 3 (11th June 2022)

• Record Type: Journal Article
• Authors: Flannery, Sean W.; Barnes, Dominique A.; Costa, Meggin Q.; Menghini, Danilo; Kiapour, Ata M.; Walsh, Edward G.; BEAR Trial Team, ; Kramer, Dennis E.; Murray, Martha M.; Fleming, Braden C.
• Is Part Of: Journal of orthopaedic research. Volume 41:Issue 3(2023)
• Page Start: 649
• Page End: 656
• Publication Date: 2022-06-11
• Subjects: ACL -- automated -- deep learning -- segmentation -- T2* relaxometry; Orthopedics -- Periodicals; Musculoskeletal system -- Periodicals; 616.7
• DOI: 10.1002/jor.25390
• Languages: English
• ISSNs: 0736-0266

Abstract

Abstract: Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T2 * relaxometry. However, T2 * mapping requires manual image segmentation, which is a time‐consuming process and prone to inter‐ and intra‐ segmenter variability. Automating segmentation would address these challenges. A model previously trained using Constructive Interference in Steady State (CISS) scans was applied to T2 * segmentation via transfer learning. It was hypothesized that there would be no significant differences in the model's segmentation performance between T2 * and CISS, structural measures versus ground truth manual segmentation, and reliability versus independent and retest manual segmentation. Transfer learning was conducted using 54 T2 * scans of the ACL. Segmentation performance was assessed with Dice coefficient, precision, and sensitivity, and structurally with T 2 * value, volume, subvolume proportions, and cross‐sectional area. Model performance relative to independent manual segmentation and repeated segmentation by the ground truth segmenter (retest) were evaluated on a random subset. Segmentation performance was analyzed with Mann–Whitney U tests, structural measures with Wilcoxon signed‐rank tests, and performance relative to manual segmentation with repeated‐measures analysis of variance/Tukey tests ( α = 0.05). T2 * segmentation performance was not significantly different from CISS on all measures ( p > 0.35). No significant differences were detected in structural measures ( p > 0.50). Automatic segmentation performed as well as the retest on all segmentation measures, whereas independent segmentations were lower than retest and/or automatic segmentation ( p < 0.023). Structural measures were not significantly different between segmenters. The automatic segmentation model performed as well on the T2 * sequence as on CISS and outperformed independent manual segmentation while performing as well as retest segmentation.