Unsupervised Learning for Feature Extraction and Temporal Alignment of 3D+t Point Clouds of Zebrafish Embryos
This addresses the need for scalable and unbiased synchronization in biomedical research on zebrafish embryos, though it is incremental as it builds on existing unsupervised and deep learning techniques.
The paper tackles the problem of synchronizing developmental stages in zebrafish embryos by proposing an unsupervised method for feature extraction and temporal alignment from 3D+t point clouds, achieving an average mismatch of only 3.83 minutes over 5.3 hours.
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.