用UKBiobank数据实现大规模睾丸体积分割，精度超人工标注。
Towards Population Scale Testis Volume Segmentation in DIXON MRI
- 基于UKBiobank的DIXON MRI数据，训练深度学习模型分割睾丸体积。
- 最佳模型中位Dice达0.87，优于人类标注者中位0.83的可靠性。
- 首次实现人群规模睾丸体积自动标注，适合生殖健康研究者使用。
睾丸大小是男性生育力的重要预测指标，临床常通过触诊或影像学评估。尽管具有潜力，基于影像学进行人群水平的睾丸体积评估仍鲜有探索。以往研究受限于小样本和偏差数据集，已证明机器学习在睾丸体积分割中的可行性。本文利用英国生物银行（UKBiobank）的磁共振成像数据，评估了睾丸体积分割方法。最佳模型在相同数据集上达到中位Dice分数0.87，优于人类标注者中位0.83的可靠性，首次实现人群规模的自动化标注。本研究旨在提供训练好的模型、可比基准方法及标注训练数据，以提升睾丸MRI分割研究的可访问性与可复现性。
原文摘要 · Abstract (English)
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 Magnet Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of $0.87$, compared to median dice score of $0.83$ 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.
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