Work

Towards population scale testis volume segmentation in DIXON MRI

Year: 2025

Type: article

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. • Deep learning for automatic testicular volume segmentation from MRI data. • UkBiobank data enables large-scale, population-level testicular volume analysis. • Achieved human-level accuracy with a 3D nnUnet approach. • Overcomes previous limitations of manual measurement and small, biased datasets. • Provides annotations and model weights to enhance reproducibility and accessibility

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2025.111139