Image Quality Ranking Method for Microscopy

Koho, Sami; Fazeli, Elnaz; Eriksson, John E.; Hänninen, Pekka

doi:10.1038/srep28962

Cited by 40 publications

(26 citation statements)

References 36 publications

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“…High entropy values indicate high contrast/noisy images while low values indicate lower contrast. These image quality measures were created with PyImq (Koho et al, 2016). Random effects linear models were created with the R package lme4 (Bates et al, 2014) by fitting log precision to taxonomic family, magnification, and imaging technique as factors.…”

Section: Manualcount Automaticcountmentioning

confidence: 99%

StomataCounter: a neural network for automatic stomata identification and counting

Fetter

Eberhardt

Barclay

et al. 2018

Preprint

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Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user friendly, fully-automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata.• We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a humanin-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images.• Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species.• To facilitate adoption of the method, we provide the method in a publicly available website at http: //www.stomata.science/.

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Section: Manualcount Automaticcountmentioning

confidence: 99%

StomataCounter: a neural network for automatic stomata identification and counting

Fetter

Eberhardt

Barclay

et al. 2018

Preprint

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“…Acquiring high quality optical microscopy images reliably can be a challenge for biologists, since individual images can be noisy, poorly exposed, out-of-focus, vignetted or unevenly illuminated, or contain dust artifacts. These types of image degradation may occur on only a small fraction of a dataset too large to survey manually, especially in high-content screening applications [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing microscope image focus quality with deep learning

et al. 2018

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BackgroundLarge 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.ResultsWe 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.ConclusionsOur 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.

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“…This suggests that the application of such NR-IQA metrics to microscopy data sets may lead to unpredictable results. Although a number of microscopy oriented NR-IQA approaches have been reported 30 – 34 , these were mainly developed to address very specific applications, what might generate similar concerns over their reliability and predictability when used in other scenarios. To the best of our knowledge, the use of NR-IQA methods in combination with PSHG data sets represents a subject that has not been previously addressed.…”

Section: Introductionmentioning

confidence: 99%

A Study on Image Quality in Polarization-Resolved Second Harmonic Generation Microscopy

Stanciu

Ávila

Hristu

et al. 2017

Sci Rep

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Second harmonic generation (SHG) microscopy represents a very powerful tool for tissue characterization. Polarization-resolved SHG (PSHG) microscopy extends the potential of SHG, by exploiting the dependence of SHG signals on the polarization state of the excitation beam. Among others, this dependence translates to the fact that SHG images collected under different polarization configurations exhibit distinct characteristics in terms of content and appearance. These characteristics hold deep implications over image quality, as perceived by human observers or by image analysis methods custom designed to automatically extract a quality factor from digital images. Our work addresses this subject, by investigating how basic image properties and the outputs of no-reference image quality assessment methods correlate to human expert opinion in the case of PSHG micrographs. Our evaluation framework is based on SHG imaging of collagen-based ocular tissues under different linear and elliptical polarization states of the incident light.Second Harmonic Generation (SHG) microscopy is regarded nowadays as a very useful and powerful tool for characterizing biological tissues 1,2 . Its potential originates from the ability to image in a label-free manner non-centrosymmetric structures, which exhibit a non-vanishing second-order susceptibility tensor χ (2) . Under the influence of an external electric field, such structures generate a nonlinear optical signal at exactly half the wavelength of the excitation source. This emission can be easily isolated from the excitation wavelength or from associated fluorescence signals by using spectral filters. Most SHG applications that focus on physiological assessment and disease diagnostics rely on imaging type-I collagen 3-6 , the most abundant protein in the human body and the main structural protein in the extracellular matrix of animal tissues. The investigation of collagen distribution in tissues with SHG enables a precise and non-invasive assessment of extracellular matrix modifications, which represent a hallmark for a wide range of pathologies, including cancer 7,8 . Polarization-resolved SHG (PSHG) microscopy extends the potential of SHG, by exploiting the fact that this nonlinear signal is sensitive to the polarization state of the excitation beam 9 . In the case of collagen-based tissues, the SHG emission depends on the alignment between the collagen fibers/fibrils and the polarization of the excitation light [10][11][12] . The additional dimension available in PSHG data sets can be used to analyze the optical anisotropy and hence to better probe the molecular organization and the external arrangement of collagen-based structures [10][11][12][13][14][15][16] . With the hierarchical organization of collagen being closely intertwined with the biophysical, mechanical and hence functional properties of most tissues, PSHG's potential can be steered towards finding answers to important questions over diseases genesis, progression and treatment. Moreover, PSHG can potentially be used ...

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Image Quality Ranking Method for Microscopy

Cited by 40 publications

References 36 publications

StomataCounter: a neural network for automatic stomata identification and counting

StomataCounter: a neural network for automatic stomata identification and counting

Assessing microscope image focus quality with deep learning

A Study on Image Quality in Polarization-Resolved Second Harmonic Generation Microscopy

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