无监督学习自动提取斑马鱼胚胎3D点云特征并精准对齐发育阶段
Unsupervised Learning for Feature Extraction and Temporal Alignment of 3D+t Point Clouds of Zebrafish Embryos
- 用自编码器学习点云的描述性特征，再通过深度回归网络实现时间对齐
- 在5.3小时实验期内平均错位仅3.83分钟，对齐精度高
- 无需人工标注，适合大规模发育研究，避免主观偏差
斑马鱼广泛用于生物医学研究，其胚胎发育阶段常需同步以便后续分析。本文提出一种无监督方法，从3D+t点云中提取描述性特征，并基于这些特征对齐对应发育阶段。采用自编码器架构学习点云的表征，设计深度回归网络实现时间对齐。在5.3小时实验周期内，平均错位仅为3.83分钟，对齐精度高。该方法完全无需人工标注，可轻松扩展，且避免了人工分析带来的主观偏差。
原文摘要 · Abstract (English)
Zebrafish are widely used in biomedical research and developmental stages of their embryos often need to be synchronized for further analysis. We present an unsupervised approach to extract descriptive features from 3D+t point clouds of zebrafish embryos and subsequently use those features to temporally align corresponding developmental stages. An autoencoder architecture is proposed to learn a descriptive representation of the point clouds and we designed a deep regression network for their temporal alignment. We achieve a high alignment accuracy with an average mismatch of only 3.83 minutes over an experimental duration of 5.3 hours. As a fully-unsupervised approach, there is no manual labeling effort required and unlike manual analyses the method easily scales. Besides, the alignment without human annotation of the data also avoids any influence caused by subjective bias.
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