Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists
Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists
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DOI：
10.1007/s10577-021-09676-z
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发表时间：
2021-10
影响因子：
2.6
通讯作者：
K. Nagaki;Tomoyuki Furuta;N. Yamaji;Daichi Kuniyoshi;Megumi Ishihara;Y. Kishima;Minoru Murata;A. Hoshino;Hirotomo Takatsuka
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文献类型：
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作者：
K. Nagaki;Tomoyuki Furuta;N. Yamaji;Daichi Kuniyoshi;Megumi Ishihara;Y. Kishima;Minoru Murata;A. Hoshino;Hirotomo Takatsuka
观察染色体是一个耗时耗力的过程，多年来染色体一直是人工分析的。在过去的十年中，用于显微图像的自动采集系统由于其控制计算机系统的进步而取得了巨大的进步，并且如今，可以自动采集由大量（超过1000个）来自大面积标本的图像组成的平铺图像集。然而，还没有简单且廉价的系统来有效地从这些图像中选择包含有丝分裂细胞的图像。本文应用了一种通过深度学习人工智能（AI）进行的染色体图像分类系统，该系统可以由非数据科学家轻松处理。有了这个系统，适合我们自己的样本的模型可以很容易地在Macintosh计算机上使用Create ML构建。作为示例，通过使用来自各种植物物种的染色体图像进行学习而构建的模型能够对除了来自所使用物种的样本之外的来自未用于学习的植物物种的样本中的包含有丝分裂细胞的图像进行分类。该系统也适用于组织切片和四分体中的细胞。由于该系统价格低廉，并且可以使用科学家自己的样本通过深度学习轻松训练，因此它不仅可以用于染色体图像分析，还可以用于其他生物学相关图像的分析。
Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists’ own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.