State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-training that learns dense image representations from raw, unlabeled live-cell microscopy videos. Our method builds upon the task of predicting the correct order of time-flipped image regions via a single-image feature extractor followed by a time arrow prediction head that operates on the fused features. We show that the resulting dense representations capture inherently time-asymmetric biological processes such as cell divisions on a pixel-level. We furthermore demonstrate the utility of these representations on several live-cell microscopy datasets for detection and segmentation of dividing cells, as well as for cell state classification. Our method outperforms supervised methods, particularly when only limited ground truth annotations are available as is commonly the case in practice. We provide code at https://github.com/weigertlab/tarrow.

翻译：当前最先进的显微图像目标检测与分割方法依赖于监督式机器学习，这需要耗费大量人力对训练数据进行人工标注。本文提出一种基于时间箭头预测预训练的自监督方法，可从原始未标记的活细胞显微视频中学习密集图像表示。该方法通过构建一个图像特征提取器结合时间箭头预测头，对时间翻转图像区域进行正确顺序预测。研究表明，所生成的密集表示能够从像素级别捕捉细胞分裂等本质时间不对称的生物过程。我们进一步在多个活细胞显微数据集上验证了这些表示在分裂细胞检测、分割及细胞状态分类中的实用性。该方法在标注数据有限（实际应用中常见情况）时仍优于监督方法。代码已开源在 https://github.com/weigertlab/tarrow。