Assessing microscope image focus quality with deep learning

Autor: Yang, Samuel J, Berndl, Marc, Michael Ando, D., Narayanaswamy, Arunachalam, Christiansen, Eric, Hoyer, Stephan, Roat, Chris, Rueden, Curtis T, Shankar, Asim, Finkbeiner, Steven, Nelson, Philip, Yang, Samuel J., Rueden, Curtis T., Barch, Mariya, Hung, Jane Yen
Přispěvatelé: Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Chemical Engineering, Barch, Mariya, Hung, Jane Yen
Jazyk: angličtina
Rok vydání: 2017
Předmět:
0301 basic medicine
Open-source
Image quality
Computer science
Image Processing
Defocus
Biochemistry
Mathematical Sciences
law.invention
Image analysis
Machine Learning
chemistry.chemical_compound
0302 clinical medicine
Computer-Assisted
Structural Biology
law
Image Processing
Computer-Assisted
Tumor Cells
Cultured
lcsh:QH301-705.5
Autofocus
Osteosarcoma
Microscopy
Cultured
Artificial neural network
Applied Mathematics
Biological Sciences
ImageJ
Computer Science Applications
Tumor Cells
Focus
lcsh:R858-859.7
Diagnostic Imaging
Bioinformatics
Bone Neoplasms
lcsh:Computer applications to medicine. Medical informatics
Stain
03 medical and health sciences
Information and Computing Sciences
Machine learning
CellProfiler
Medical imaging
Humans
Molecular Biology
Pixel
business.industry
Deep learning
Pattern recognition
Hoechst stain
030104 developmental biology
chemistry
lcsh:Biology (General)
Artificial intelligence
Focus (optics)
business
030217 neurology & neurosurgery
Software
Zdroj: BioMed Central
BMC bioinformatics, vol 19, iss 1
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-9 (2018)
BMC Bioinformatics
Popis: Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. Results We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Conclusions Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.
Databáze: OpenAIRE
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