Dataset Information

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

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 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.

SUBMITTER: Flannery SW

PROVIDER: S-EPMC9708947 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Flannery Sean W SW Barnes Dominique A DA Costa Meggin Q MQ Menghini Danilo D Kiapour Ata M AM Walsh Edward G EG Bear Trial Team Kramer Dennis E DE Murray Martha M MM Fleming Braden C BC

Journal of orthopaedic research : official publication of the Orthopaedic Research Society 20220611 3

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 hypothesize ...[more]

PMID: 35634860

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Project description:Quantitative magnetic resonance imaging enables quantitative assessment of the healing anterior cruciate ligament or graft post-surgery, but its use is constrained by the need for time consuming manual image segmentation. The goal of this study was to validate a deep learning model for automatic segmentation of repaired and reconstructed anterior cruciate ligaments. We hypothesized that (1) a deep learning model would segment repaired ligaments and grafts with comparable anatomical similarity to intact ligaments, and (2) automatically derived quantitative features (i.e., signal intensity and volume) would not be significantly different from those obtained by manual segmentation. Constructive Interference in Steady State sequences were acquired of ACL repairs (n = 238) and grafts (n = 120). A previously validated model for intact ACLs was retrained on both surgical groups using transfer learning. Anatomical performance was measured with Dice coefficient, sensitivity, and precision. Quantitative features were compared to ground truth manual segmentation. Automatic segmentation of both surgical groups resulted in decreased anatomical performance compared to intact ACL automatic segmentation (repairs/grafts: Dice coefficient = .80/.78, precision = .79/.78, sensitivity = .82/.80), but neither decrease was statistically significant (Kruskal-Wallis: Dice coefficient p = .02, precision p = .09, sensitivity p = .17; Dunn post-hoc test for Dice coefficient: repairs/grafts p = .054/.051). There were no significant differences in quantitative features between the ground truth and automatic segmentation of repairs/grafts (0.82/2.7% signal intensity difference, p = .57/.26; 1.7/2.7% volume difference, p = .68/.72). The anatomical similarity performance and statistical similarities of quantitative features supports the use of this automated segmentation model in quantitative magnetic resonance imaging pipelines, which will accelerate research and provide a step towards clinical applicability.

Dataset Information

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

Publications

Automated segmentation of the healed anterior cruciate ligament from T<sub>2</sub> * relaxometry MRI scans.

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