IMACEL: A cloud-based bioimage analysis platform for morphological analysis and image classification

Automated quantitative image analysis is essential for all fields of life science research. Although several software programs and algorithms have been developed for bioimage processing, an advanced knowledge of image processing techniques and high-performance computing resources are required to use them. Hence, we developed a cloud-based image analysis platform called IMACEL, which comprises morphological analysis and machine learning-based image classification. The unique click-based user interface of IMACEL’s morphological analysis platform enables researchers with limited resources to evaluate particles rapidly and quantitatively without prior knowledge of image processing. Because all the image processing and machine learning algorithms are performed on high-performance virtual machines, users can access the same analytical environment from anywhere. A validation study of the morphological analysis and image classification of IMACEL was performed. The results indicate that this platform is an accessible and potentially powerful tool for the quantitative evaluation of bioimages that will lower the barriers to life science research.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Gaudenz Danuser,et al.  Computer Vision in Cell Biology , 2011, Cell.

[3]  Anatole Chessel,et al.  An Overview of data science uses in bioimage informatics. , 2017, Methods.

[4]  Kevin W. Eliceiri,et al.  ImageJ2: ImageJ for the next generation of scientific image data , 2017, BMC Bioinformatics.

[5]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[6]  Fumio Hasegawa,et al.  Stemness and anti‐cancer drug resistance in ATP‐binding cassette subfamily G member 2 highly expressed pancreatic cancer is induced in 3D culture conditions , 2018, Cancer science.

[7]  Koichi Fukunaga,et al.  Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments , 2017, Scientific Reports.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Christoph Sommer,et al.  Machine learning in cell biology – teaching computers to recognize phenotypes , 2013, Journal of Cell Science.

[10]  Natsumaro Kutsuna,et al.  Contribution of anaphase B to chromosome separation in higher plant cells estimated by image processing. , 2007, Plant & cell physiology.

[11]  Hanchuan Peng,et al.  Bioimage Informatics for Big Data. , 2016, Advances in anatomy, embryology, and cell biology.

[12]  K. Scharf,et al.  Cytoplasmic heat shock granules are formed from precursor particles and are associated with a specific set of mRNAs , 1989, Molecular and cellular biology.

[13]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  M J Schlesinger,et al.  The dynamic state of heat shock proteins in chicken embryo fibroblasts , 1986, The Journal of cell biology.

[15]  Yasuyuki Nemoto,et al.  Tobacco BY-2 Cell Line as the “HeLa” Cell in the Cell Biology of Higher Plants , 1992 .

[16]  Seiichiro Hasezawa,et al.  Cell cycle synchronization of tobacco BY-2 cells , 2006, Nature Protocols.

[17]  Tomoshi Otsuki,et al.  Active learning framework with iterative clustering for bioimage classification , 2012, Nature Communications.

[18]  Anne E Carpenter,et al.  Reconstructing cell cycle and disease progression using deep learning , 2017, Nature Communications.

[19]  K. Scharf,et al.  Formation of cytoplasmic heat shock granules in tomato cell cultures and leaves , 1983, Molecular and cellular biology.

[20]  A. I.,et al.  Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks , 2023, Biology.

[21]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..