CS3: Cascade SAM for Sperm Segmentation

TL;DR

The paper introduces CS3, an unsupervised cascade approach using SAM to improve sperm segmentation, especially in overlapping cases, validated on a new dataset with superior performance over existing methods.

Contribution

It presents a novel cascade SAM-based method specifically designed for sperm segmentation, addressing overlap issues without requiring extensive labeled data.

Findings

CS3 outperforms existing segmentation methods.

Enhanced segmentation accuracy in overlapping sperm images.

Effective use of unlabeled data for model fine-tuning.

Abstract

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.