Automated segmentation of the healed anterior cruciate ligament from T2 * relaxometry MRI scans.

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 T2 * 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. © 2022 Orthopaedic Research Society. Published by Wiley Periodicals LLC.

Citation

Sean W Flannery, Dominique A Barnes, Meggin Q Costa, Danilo Menghini, Ata M Kiapour, Edward G Walsh, Bear Trial Team, Dennis E Kramer, Martha M Murray, Braden C Fleming. Automated segmentation of the healed anterior cruciate ligament from T2 * relaxometry MRI scans. Journal of orthopaedic research : official publication of the Orthopaedic Research Society. 2023 Mar;41(3):649-656

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PMID: 35634860

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