PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The study cohort consisted of retrospectively collected patient data (N = 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.
UNASSIGNED: 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 (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 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.
UNASSIGNED: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.
UNASSIGNED: 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.
UNASSIGNED: The study cohort consisted of retrospectively collected patient data (N = 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.
UNASSIGNED: 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 (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 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.
UNASSIGNED: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.
摘要：
■睾丸体积（TV）是监测睾丸功能和病理的必要参数。然而，电流测量工具，包括睾丸测定仪和超声检查,在获得准确和个性化的电视测量时遇到挑战。
■基于磁共振成像（MRI），这项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电视方面的功效。
■研究队列包括回顾性收集的患者数据（N=200）和前瞻性收集的数据集，包括10名健康志愿者。回顾性数据集分为训练集和独立验证集，8:2随机分布。10名健康志愿者中的每一个经历5次扫描(形成测试数据集)以评估测量再现性。应用ResUNet算法对测试进行分段。通过将体素体积乘以体素的数量来计算每个睾丸的体积。专家手动确定的面具被用作评估深度学习模型性能的基础事实。
■深度学习模型在验证队列中的平均Dice评分为0.926±0.034（左睾丸为0.921±0.026，右睾丸为0.926±0.034），在测试队列中的平均Dice评分为0.922±0.02（左睾丸为0.931±0.019，右睾丸为0.932±0.022）。手动电视和自动电视之间存在很强的相关性（验证队列中的R2范围为0.974至0.987；测试队列中的R2范围为0.936至0.973）。在验证队列中，手动和自动测量之间的体积差异为0.838±0.991（LTV为0.209±0.665，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时表现出优异的可重复性(组内相关性>0.9).
■基于MRI的深度学习模型是测量电视的准确可靠的工具。
■基于磁共振成像（MRI），这项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电视方面的功效。
■研究队列包括回顾性收集的患者数据（N=200）和前瞻性收集的数据集，包括10名健康志愿者。回顾性数据集分为训练集和独立验证集，8:2随机分布。10名健康志愿者中的每一个经历5次扫描(形成测试数据集)以评估测量再现性。应用ResUNet算法对测试进行分段。通过将体素体积乘以体素的数量来计算每个睾丸的体积。专家手动确定的面具被用作评估深度学习模型性能的基础事实。
■深度学习模型在验证队列中的平均Dice评分为0.926±0.034（左睾丸为0.921±0.026，右睾丸为0.926±0.034），在测试队列中的平均Dice评分为0.922±0.02（左睾丸为0.931±0.019，右睾丸为0.932±0.022）。手动电视和自动电视之间存在很强的相关性（验证队列中的R2范围为0.974至0.987；测试队列中的R2范围为0.936至0.973）。在验证队列中，手动和自动测量之间的体积差异为0.838±0.991（LTV为0.209±0.665，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时表现出优异的可重复性(组内相关性>0.9).
■基于MRI的深度学习模型是测量电视的准确可靠的工具。
