基于增强的YOLOv4和改进的DeepSORT的多对象精子检测和跟踪
Jihong Pei1, Zhenting Yang1, Yang Zhao2
1College of Electronics and Information Engineering, Shenzhen University, ShenZhen, 518060, China.
Scientific reports
|November 27, 2025
概括
这项研究引入了计算机辅助精液分析的先进框架,改善了微观图像中的精子检测和跟踪. 改进的系统有效地处理小和模糊的精子目标,推进不孕症诊断.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 生殖医学 生殖医学
背景情况:
- 辅助生殖技术对于不孕症治疗至关重要.
- 当前的计算机辅助精液分析与小而模糊的精子图像作斗争.
- 准确的精子分析对于有效的不孕症解决方案至关重要.
研究的目的:
- 开发一个先进的多对象精子检测和跟踪框架.
- 克服当前计算机辅助精液分析系统的局限性.
- 为了提高精子分析用于不孕症诊断的准确性.
主要方法:
- 集成了一个增强的YOLOv4架构与一个优化的DeepSORT算法.
- 实施一种新的图像切片策略,以防止小目标损失.
- 利用ResNet50进行了改进的特征提取和专门的精子图像增强策略.
主要成果:
- 在精子检测和跟踪指标方面的卓越性能.
- 强大的检测和跟踪多个小精子目标.
- 在准确性和效率方面超越现有的最先进的方法.
结论:
- 拟议的框架大大改善了计算机辅助精液分析.
- 该系统为检测和跟踪精子,特别是小目标提供了更高的准确性.
- 这一进步有望为更有效的不孕症诊断和治疗提供希望.
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