Sun Kailun, Fan Chanyuan, Feng Zhaoyan, Min Xiangde, Wang Yu, Sun Ziyan, Li Yan, Cai Wei, Yin Xi, Zhang Peipei, Liu Qiuyu, Xia Liming
Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Med (Lausanne). 2023 Sep 19;10:1277535. doi: 10.3389/fmed.2023.1277535. eCollection 2023.
Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.
Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.
The study cohort consisted of retrospectively collected patient data ( = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.
The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV ( ranging from 0.974 to 0.987 in the validation cohort; R ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV.
The MRI-based deep learning model is an accurate and reliable tool for measuring TV.
睾丸体积（TV）是监测睾丸功能和病变的重要参数。然而，目前的测量工具，包括睾丸体积测量器和超声检查，在获得准确和个性化的TV测量值方面存在挑战。
基于磁共振成像（MRI），本研究旨在建立一个深度学习模型，并评估其在分割睾丸和测量TV方面的有效性。
研究队列包括回顾性收集的患者数据（n = 200）和前瞻性收集的包含10名健康志愿者的数据集。回顾性数据集以8:2的随机分布分为训练集和独立验证集。10名健康志愿者每人接受5次扫描（形成测试数据集）以评估测量的可重复性。应用ResUNet算法分割睾丸。通过将体素体积乘以体素数量来计算每个睾丸的体积。专家手动确定的掩码用作评估深度学习模型性能的金标准。
在验证队列中，深度学习模型的平均Dice分数为0.926±0.034（左侧睾丸为0.921±0.026，右侧睾丸为0.926±0.034），在测试队列中平均Dice分数为0.922±0.02（左侧睾丸为0.931±0.019，右侧睾丸为0.932±0.022）。手动测量和自动测量的TV之间存在很强的相关性（验证队列中R范围为0.974至0.987；测试队列中R范围为0.936至0.973）。在验证队列中，手动测量和自动测量之间的体积差异为0.838±0.991（左侧睾丸体积（LTV）为0.209±0.665，右侧睾丸体积（RTV）为0.630±0.728），在测试队列中为0.815±0.824（LTV为0.303±0.664，RTV为0.511±0.444）。此外，深度学习模型在确定TV方面表现出出色的可重复性（组内相关性>0.9）。
基于MRI的深度学习模型是测量TV的准确可靠工具。
Zotero 插件
