ORIGINAL RESEARCH article
Front. Phys.
Sec. Medical Physics and Imaging
This article is part of the Research TopicMulti-Modality Based AI in Biomedical Physics for Disease Diagnosis and TreatmentView all 4 articles
Automated Segmentation of the Lacrimal Gland on Pre-versus Post-Contrast T1-weighted MRI Sequences
Provisionally accepted
Amogh Shetty1
Ramkumar Rajabathar Babu Jai Shanker2
Mathew Illimoottil3
Qasim Chohdry3Shrinidhi Kadkol4Sarah Illimoottil3Matthew Holliman5
Daniel Ginat5*- 1partment of Biology, Rensselaer Polytechnic Institute, troy, United States
- 2University of Chicago, chicago, United States
- 3University of Missouri-Kansas City School of Medicine, Kansas City, United States
- 4University of Illinois Chicago College of Medicine, Chicago, United States
- 5The University of Chicago, Chicago, United States
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Purpose: The 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. Methods: Eighty-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. Results: POST-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. Conclusions: CNN-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.
Keywords: artificial intelligence, Contrast enhanced MRI, fat suppression, lacrimal gland, Magnetic Resonance Imaging
Received: 02 Sep 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Shetty, Rajabathar Babu Jai Shanker, Illimoottil, Chohdry, Kadkol, Illimoottil, Holliman and Ginat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daniel Ginat
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