2020年12月14日，美国霍华德.休斯医学院Marius Pachitariu课题组在《自然-方法学》杂志发表论文，宣布他们开发出细胞分离的通用算法：Cellpose。
在本研究中，研究人员介绍了一种基于深度学习的通用方法Cellpose，该方法可以从各种类型图像中精确地分离出细胞，并且不需要优化模型或调整参数。研究人员在高度变化的新细胞图像数据集上对Cellpose进行了优化，该数据集包含了超过70,000个分离对象。
研究人员还揭示了Cellpose在三维（3D）扩展中的应用，该扩展重用了二维（2D）模型，并且不需要3D标记数据。为了方便Cellpose对发表数据的应用，研究人员研发了用于手动标记和管理自动结果的软件。定期利用已发表数据对模型进行重新校正，将确保Cellpose不断得到改进。
据介绍，许多生物学研究需要从显微镜图像中分辨细胞主体、细胞膜和细胞核。深度学习已在此应用上取得了长足进步，但是当前的方法专用于具有大量数据集的图像。
附：英文原文
Title: Cellpose: a generalist algorithm for cellular segmentation
Author: Carsen Stringer, Tim Wang, Michalis Michaelos, Marius Pachitariu
Issue&Volume: 2020-12-14
Abstract: Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly. Cellpose is a generalist, deep learning-based approach for segmenting structures in a wide range of image types. Cellpose does not require parameter adjustment or model retraining and outperforms established methods on 2D and 3D datasets.
DOI: 10.1038/s41592-020-01018-x
Source: https://www.nature.com/articles/s41592-020-01018-x
Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex
本期文章：《自然—方法学》：Online/在线发表
