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 and a subsequent time arrow prediction head. 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：目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手动标注。本文提出一种基于时间箭头预测预训练的自监督方法，能够从原始未标注的活细胞显微视频中学习密集图像表示。该方法通过单图像特征提取器及后续的时间箭头预测头，完成预测时间翻转图像区域正确顺序的任务。研究表明：所获得的密集表示能够在像素级别捕捉具有固有时间不对称性的生物过程（如细胞分裂）。我们进一步在多个活细胞显微数据集上展示了这些表示在分裂细胞检测与分割、细胞状态分类中的实用性。本方法尤其在标注数据稀缺的实际场景中表现优于监督方法。相关代码已开源至 https://github.com/weigertlab/tarrow。