再培训
计算机科学
分割
人工智能
通才与专种
深度学习
图像分割
训练集
航程(航空)
机器学习
模式识别(心理学)
三维模型
计算机视觉
生物
国际贸易
复合材料
栖息地
材料科学
业务
生态学
作者
Carsen Stringer,Yichen Wang,Michalis Michaelos,Marius Pachitariu
出处
期刊:Nature Methods
[Springer Nature]
日期:2020-12-14
卷期号:18 (1): 100-106
被引量:1348
标识
DOI:10.1038/s41592-020-01018-x
摘要
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.
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