Automated segmentation of the lacrimal gland on non-contrast versus post-contrast T1-weighted MRI sequences - Shetty, Amogh; Babu Jai Shanker, Ramkumar Rajabathar; Illimoottil, Mathew; Chohdry, Qasim; Kadkol, Shrinidhi; Illimoottil, Sarah; Holliman, Matthew; Ginat, Daniel T. (Frontiers in Physics, 2026)
PurposeThe lacrimal glands are small orbital exocrine structures responsible for tear production. Segmentation on MRI is challenging due to their small size, low contrast with adjacent tissues, and partial representation across slices. This study evaluates U-Net based models for automated lacrimal gland segmentation on non-contrast T1-weighted (AX-T1) and contrast-enhanced fat-suppressed (POST-AX-T1-FS) MRI.MethodsEighty-six patients with high-resolution orbital MRI were retrospectively analyzed. Manual gland annotations were created in 3D Slicer. A U-Net architecture was trained with 4-fold cross-validation on an 80:20 train-test split. Performance was assessed on a hold-out set using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Hausdorff Distance.ResultsPOST-AX-T1-FS achieved the highest performance (mean DSC 0.79 ± 0.19, IoU 0.68 ± 0.19), outperforming AX-T1. Volume correlation with ground truth was 0.81 for POST-AX-T1-FS and 0.71 for AX-T1. Most errors were false negatives in abnormal gland morphology. Qualitative review showed anatomically consistent segmentations, especially with region-prioritized sampling.ConclusionCNN-based models show ability to segment lacrimal glands from orbital MRI, though performance is moderate with Dice scores around 0.79. Non-contrast sequences may provide reasonably accurate segmentations, but further refinement and broader validation are required. With continued optimization and larger, more diverse datasets, these models may eventually support more consistent gland delineation in research and early exploratory clinical use.