3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
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DOI：
10.7554/elife.59187
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发表时间：
2021-03-30
期刊：
影响因子：
7.7
通讯作者：
Kimura KD
中科院分区：
文献类型：
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作者：
Wen C;Miura T;Voleti V;Yamaguchi K;Tsutsumi M;Yamamoto K;Otomo K;Fujie Y;Teramoto T;Ishihara T;Aoki K;Nemoto T;Hillman EM;Kimura KD
尽管近年来显微镜技术有所进步，但在三维延时图像（3D + T图像）中分割和跟踪细胞以提取其动态位置和活动仍然是该领域的一个相当大的瓶颈。我们开发了一个基于深度学习的软件管道，3DeeCellTracker，通过集成多种现有的和新的技术，包括深度学习跟踪。仅使用一卷训练数据，一次初始校正和一些参数更改，3DeeCellTracker成功地分割和跟踪了半固定和“拉直”自由移动的蠕虫大脑，自然跳动的斑马鱼心脏中的约100个细胞，以及3D培养的肿瘤球体中的约1000个细胞。虽然这些数据集是用高度发散的光学系统成像的，但我们的方法在大多数情况下追踪了90-100%的细胞，这与以前的结果相当或更好。这些结果表明，3DeeCellTracker可以为揭示难以分析的图像数据集中的动态细胞活动铺平道路。自17世纪以来，显微镜就被用来解密生命的微小细节。现在，3D显微镜的出现使科学家能够建立活细胞和组织的详细图片。在这项工作中，自动化变得越来越重要，这样科学家就可以分析得到的图像，了解身体是如何生长、愈合和对药物治疗等变化做出反应的。特别是，算法可以帮助识别图像中的细胞（称为细胞分割），然后在多个图像中随时间跟踪这些细胞（称为细胞跟踪）。然而，在给定时期内对3D图像进行这些分析是相当具有挑战性的。此外，已经创建的算法通常不是用户友好的，它们只能应用于通过特定科学方法收集的特定数据集。作为回应，Wen等人开发了一个名为3DeeCellTracker的新程序，该程序在台式计算机上运行，并使用一种被称为深度学习的人工智能来产生一致的结果。至关重要的是，3DeeCellTracker可以用于分析使用不同类型的尖端显微镜系统拍摄的各种类型的图像。事实上，该算法随后被用来追踪移动的微型蠕虫的神经细胞活动，小鱼跳动的心脏细胞活动，以及实验室中生长的癌细胞活动。这种多功能工具现在可以用于生物学、医学研究和药物开发，以帮助监测细胞活动。
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and ‘straightened’ freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90–100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze. Microscopes have been used to decrypt the tiny details of life since the 17th century. Now, the advent of 3D microscopy allows scientists to build up detailed pictures of living cells and tissues. In that effort, automation is becoming increasingly important so that scientists can analyze the resulting images and understand how bodies grow, heal and respond to changes such as drug therapies. In particular, algorithms can help to spot cells in the picture (called cell segmentation), and then to follow these cells over time across multiple images (known as cell tracking). However, performing these analyses on 3D images over a given period has been quite challenging. In addition, the algorithms that have already been created are often not user-friendly, and they can only be applied to a specific dataset gathered through a particular scientific method. As a response, Wen et al. developed a new program called 3DeeCellTracker, which runs on a desktop computer and uses a type of artificial intelligence known as deep learning to produce consistent results. Crucially, 3DeeCellTracker can be used to analyze various types of images taken using different types of cutting-edge microscope systems. And indeed, the algorithm was then harnessed to track the activity of nerve cells in moving microscopic worms, of beating heart cells in a young small fish, and of cancer cells grown in the lab. This versatile tool can now be used across biology, medical research and drug development to help monitor cell activities.