# Automated Sperm Morphology Analysis Based on Instance-Aware Part Segmentation

arXiv: https://arxiv.org/abs/2408.00112 · HTML: https://mlsanity.com/paper/2408.00112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 20 of 20)

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- [Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing](https://mlsanity.com/paper/1911.12053/) (2019)
- [AIParsing: Anchor-free Instance-level Human Parsing](https://mlsanity.com/paper/2207.06854/) (2022)
- [The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation](https://mlsanity.com/paper/2203.16415/) (2022)
- [Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images](https://mlsanity.com/paper/2106.02385/) (2021)

## Abstract (arXiv)

Traditional sperm morphology analysis is based on tedious manual annotation. Automated morphology analysis of a high number of sperm requires accurate segmentation of each sperm part and quantitative morphology evaluation. State-of-the-art instance-aware part segmentation networks follow a "detect-then-segment" paradigm. However, due to sperm's slim shape, their segmentation suffers from large context loss and feature distortion due to bounding box cropping and resizing during ROI Align. Moreover, morphology measurement of sperm tail is demanding because of the long and curved shape and its uneven width. This paper presents automated techniques to measure sperm morphology parameters automatically and quantitatively. A novel attention-based instance-aware part segmentation network is designed to reconstruct lost contexts outside bounding boxes and to fix distorted features, by refining preliminary segmented masks through merging features extracted by feature pyramid network. An automated centerline-based tail morphology measurement method is also proposed, in which an outlier filtering method and endpoint detection algorithm are designed to accurately reconstruct tail endpoints. Experimental results demonstrate that the proposed network outperformed the state-of-the-art top-down RP-R-CNN by 9.2% [AP]_vol^p, and the proposed automated tail morphology measurement method achieved high measurement accuracies of 95.34%,96.39%,91.2% for length, width and curvature, respectively.