Background and objective

Infertility affects approximately one in ten couples, and almost half of the infertility cases are due to the malefactor. To diagnose infertility and determine future treatment, a semen analysis is performed. Evaluation of sperm morphology is one of several steps in semen analysis, in which the shape and size of sperm parts are examined. The laboratories dedicated to this use traditional methods susceptible to errors. An alternative to replace the poor visual ability to assess sperm size and shape is to analyze sperm morphology with a computer's help. However, since the automatic sperm classification rates do not show an acceptable precision rate for use in the clinical setting, it is considered an exciting approach to focus efforts on improving the precision in sperm segmentation to extract the contour sperm before classification. This work aims to assess the utility of two image segmentation deep learning models for segmenting human sperm heads, acrosome, and nucleus.

Methods

In this work, we evaluate the use of two well-known deep learning architectures (U-Net and Mask-RCNN) to segment parts of human sperm cells using data augmentation, cross-validation, hyperparameter tuning, and transfer learning. The experimental results are carried out using SCIAN-SpermSegGS, a public dataset with more than two hundred manually segmented sperm cells and widely used to validate segmentation methods of human sperm parts.

Results

Experimental evaluation shows that U-net with transfer learning achieves up to 95% overlapping against hand-segmented masks for sperm head (0.96), acrosome (0.94), and nucleus (0.95), using Dice coefficient as the evaluation metric. These results outperform state-of-the-art sperm parts segmentation methods.

Conclusions

The impact of transfer learning is substantial, significantly improving the results of state-of-the-art methods with a higher Dice coefficient, less dispersion, and fewer cases where the model failed to segment sperm parts. These results represent a promising advance in the ultimate goal of performing computer-assisted morphological sperm analysis.

中文翻译：

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使用深度学习的迁移学习对人类精子分割的影响

背景与目的

不孕症影响大约十分之一的夫妇，几乎一半的不孕症病例是由犯罪者造成的。为了诊断不孕症并确定未来的治疗方法，需要进行精液分析。精子形态评估是精液分析中的几个步骤之一，其中检查精子部分的形状和大小。致力于此的实验室使用容易出错的传统方法。替代评估精子大小和形状的视觉能力差的另一种方法是在计算机的帮助下分析精子形态。然而，由于自动精子分类率在临床环境中没有显示出可接受的准确率，因此集中精力提高精子分割的精度以在分类前提取轮廓精子被认为是一种令人兴奋的方法。

方法

在这项工作中，我们评估使用两种著名的深度学习架构（U-Net 和 Mask-RCNN）使用数据增强、交叉验证、超参数调整和转移学习来分割人类精子细胞的部分。实验结果是使用 SCIAN-SpermSegGS 进行的，SCIAN-SpermSegGS 是一个公共数据集，拥有 200 多个手动分割的精子细胞，广泛用于验证人类精子部分的分割方法。

结果

实验评估表明，使用 Dice 系数作为评估指标，具有迁移学习的 U-net 与手动分割的精子头部 (0.96)、顶体 (0.94) 和细胞核 (0.95) 掩模的重叠率高达 95 %。这些结果优于最先进的精子部分分割方法。

结论

迁移学习的影响是巨大的，显着改善了具有更高 Dice 系数、更少分散以及模型无法分割精子部分的情况的最先进方法的结果。这些结果代表了执行计算机辅助形态精子分析的最终目标的一个有希望的进步。