Automated sperm morphology analysis plays a crucial role in the assessment of male fertility, yet its efficacy is often compromised by the challenges in accurately segmenting sperm images. Existing segmentation techniques, including the Segment Anything Model(SAM), are notably inadequate in addressing the complex issue of sperm overlap-a frequent occurrence in clinical samples. Our exploratory studies reveal that modifying image characteristics by removing sperm heads and easily segmentable areas, alongside enhancing the visibility of overlapping regions, markedly enhances SAM's efficiency in segmenting intricate sperm structures. Motivated by these findings, we present the Cascade SAM for Sperm Segmentation (CS3), an unsupervised approach specifically designed to tackle the issue of sperm overlap. This method employs a cascade application of SAM to segment sperm heads, simple tails, and complex tails in stages. Subsequently, these segmented masks are meticulously matched and joined to construct complete sperm masks. In collaboration with leading medical institutions, we have compiled a dataset comprising approximately 2,000 unlabeled sperm images to fine-tune our method, and secured expert annotations for an additional 240 images to facilitate comprehensive model assessment. Experimental results demonstrate superior performance of CS3 compared to existing methods.

翻译：自动化精子形态分析在评估男性生育能力中起着至关重要的作用，但其有效性常因精子图像准确分割的挑战而受到限制。现有的分割技术，包括Segment Anything Model（SAM），在处理精子重叠这一临床样本中常见复杂问题时明显不足。我们的探索性研究表明，通过移除精子头部和易于分割的区域来修改图像特征，同时增强重叠区域的可见性，能显著提升SAM在分割复杂精子结构时的效率。受这些发现启发，我们提出了用于精子分割的级联SAM（CS3），这是一种专门为解决精子重叠问题而设计的无监督方法。该方法采用SAM的级联应用，分阶段分割精子头部、简单尾部及复杂尾部。随后，对这些分割出的掩模进行精细匹配与连接，以构建完整的精子掩模。我们与顶尖医疗机构合作，收集了包含约2000张未标注精子图像的数据集以微调我们的方法，并获得了另外240张图像的专家标注，以支持全面的模型评估。实验结果表明，CS3的性能优于现有方法。