Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Department of Urology, University of Utah Health, Salt Lake City, UT 84108, USA.
Asian J Androl. 2021 Mar-Apr;23(2):135-139. doi: 10.4103/aja.aja_66_20.
Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.
在进行体外受精时，处理人睾丸组织样本时，精子鉴定和选择是一项基本任务。在人睾丸活检样本中定位和识别精子细胞是一项劳动密集型且耗时的工作。我们开发了一种新的计算机辅助精子分析（CASA）系统，该系统利用深度学习在睾丸精子提取（TESE）方面实现了接近人类水平的性能，是在自定义数据集上进行训练的。该系统可自动识别睾丸活检样本中的精子。我们收集了来自 30 名患者的 702 张去识别睾丸活检样本图像的数据集。每张图像都经过归一化处理，并通过眩光滤波器和衍射校正。数据分别以 80%、10%和 10%的比例分为训练集、验证集和测试集。然后，在该数据集上训练一个由特征提取网络和目标检测网络组成的深度目标检测网络。该模型在检测任务方面与胚胎学家的表现进行了基准测试。我们的深度学习 CASA 系统在我们的数据集上的平均精度（mAP）为 0.741，平均召回率（AR）为 0.376。我们提出的方法可以实时工作；其速度实际上仅受显微镜成像速度的限制。我们的结果表明，基于深度学习的技术可以提高在睾丸活检样本中寻找精子的效率。
