Automated magnetic resonance image segmentation of the anterior cruciate ligament.

ABSTRACT: The objective of this study was to develop an automated segmentation method for the anterior cruciate ligament that is capable of facilitating quantitative assessments of the ligament in clinical and research settings. A modified U-Net fully convolutional network model was trained, validated, and tested on 246 Constructive Interference in Steady State magnetic resonance images of intact anterior cruciate ligaments. Overall model performance was assessed on the image set relative to an experienced (>5 years) "ground truth" segmenter in two domains: anatomical similarity and the accuracy of quantitative measurements (i.e., signal intensity and volume) obtained from the automated segmentation. To establish model reliability relative to manual segmentation, a subset of the imaging data was res

SUBMITTER: Flannery SW 

PROVIDER: S-EPMC8005419 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature