用稀疏点标注实现显微图像中活细胞的高效计数
CellNet -- Localizing Cells using Sparse and Noisy Point Annotations
- 基于回归的深度学习，仅需稀疏点标注即可完成细胞检测与计数
- 在低数据场景下性能优于零样本方法，减少标注负担
- 适合生物基因筛选等需要高频细胞计数的研究场景
在生物研究中，活细胞计数是关键步骤。我们与桑格研究所合作开展大规模饱和基因编辑筛查，需反复进行大量细胞计数。计算机视觉自动化可提升通量与资源效率。本文提出一种基于回归的深度学习算法，用于相位对比显微图像中的细胞检测与计数。为降低标注成本（实际中常成为瓶颈），我们仅使用快速易获取的稀疏点标注。相比当前最优的零样本方法，实验表明回归式计数在低数据条件下更具潜力。该方法助力人类基因组研究，代码已开源：https://github.com/beijn/cellnet。
原文摘要 · Abstract (English)
Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes in humans via large scale saturation genome editing screening, which requires repeatedly counting cells a great number of times. Computer Vision based automation is crucial for high throughput and resource efficiency. In this work, we develop a regression-based deep learning computer vision algorithm to detect and count cells in phase-contrast microscopy images. To reduce annotation effort, which in practice often becomes a bottleneck, we focus on counting cells only using sparse point annotations, which are fast and easy to acquire. By comparison to state-of-the-art 0-shot methods, we show that regression-based counting is a promising alternative in low data regimes. Through developing methods to automatically count living cells in microscopy images, we contribute to valuable research on the human genome. The code is available at https://github.com/beijn/cellnet.
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