SpeHeatal: A Cluster-Enhanced Segmentation Method for Sperm Morphology Analysis

TL;DR

SpeHeatal is an unsupervised segmentation method that combines a novel clustering algorithm and the Segment Anything Model to accurately segment sperm heads and tails, especially in overlapping and impurity-laden images.

Contribution

It introduces Con2Dis, a new clustering algorithm for overlapping tail segmentation, and combines it with SAM for comprehensive sperm morphology analysis without requiring large annotated datasets.

Findings

Outperforms existing methods in overlapping sperm segmentation

Effectively filters dye impurities from sperm images

Achieves high accuracy in complex sperm morphology images

Abstract

The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeatal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeatal employs the Segment Anything Model(SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeatal, particularly in handling images with overlapping sperm.