A transfer learning approach for automatic segmentation of the surgically treated anterior cruciate ligament.

ABSTRACT: 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).

SUBMITTER: Flannery SW 

PROVIDER: S-EPMC8285460 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature