研究開發一種通用的細胞分離算法
作者：
小柯機器人發布時間：2020/12/15 16:20:36
2020年12月14日，美國霍華德.休斯醫學院Marius Pachitariu課題組在《自然-方法學》雜誌發表論文，宣布他們開發出細胞分離的通用算法：Cellpose。
在本研究中，研究人員介紹了一種基於深度學習的通用方法Cellpose，該方法可以從各種類型圖像中精確地分離出細胞，並且不需要優化模型或調整參數。研究人員在高度變化的新細胞圖像數據集上對Cellpose進行了優化，該數據集包含了超過70,000個分離對象。
研究人員還揭示了Cellpose在三維（3D）擴展中的應用，該擴展重用了二維（2D）模型，並且不需要3D標記數據。為了方便Cellpose對發表數據的應用，研究人員研發了用於手動標記和管理自動結果的軟體。定期利用已發表數據對模型進行重新校正，將確保Cellpose不斷得到改進。
據介紹，許多生物學研究需要從顯微鏡圖像中分辨細胞主體、細胞膜和細胞核。深度學習已在此應用上取得了長足進步，但是當前的方法專用於具有大量數據集的圖像。
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