Towards population scale testis volume segmentation in DIXON MRI.
Authors
Affiliations (5)
Affiliations (5)
- Institute for Geoinformatics, University of Münster, Münster, Germany; Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany; University of Münster, Institute for Translational Psychiatry, Münster, Germany. Electronic address: [email protected].
- Department of General Pediatrics, University of Münster, Münster, Germany.
- Institute for Geoinformatics, University of Münster, Münster, Germany; Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
- University Hospital Münster, Department of Nuclear Medicine, Münster, Germany.
- University of Münster, Institute for Translational Psychiatry, Münster, Germany.
Abstract
Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for testis volume segmentation. This paper presents an evaluation of segmentation methods for testicular volume using Magnetic Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of 0.89, compared to median dice score of 0.85 for human interrater reliability on the same dataset, enabling large-scale annotation on a population scale for the first time. Our overall aim is to provide a trained model, comparative baseline methods, and annotated training data to enhance accessibility and reproducibility in testis MRI segmentation research.