Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology

Campanella, Gabriele; Rajanna, Arjun Raj; Corsale, Lorraine; Schüffler, Peter J.; Yagi, Yukako; Fuchs, Thomas J.

doi:10.1016/j.compmedimag.2017.09.001

Cited by 47 publications

(51 citation statements)

References 16 publications

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“…Prior works in synthetic data generation for training focus quality predictors have leveraged Gaussian blurring for histopathology [9] and Gaussian blurring with Poisson noise for cytology specimens from transmission light microscopes [8,9] . By contrast, our work systematically demonstrates the value of adding Poisson noise and JPEG compression artifacts in histopathology, and using a synthetic blur (Bokeh) that more closely mimics real OOF.…”

Section: Discussionmentioning

confidence: 99%

“…Overview Given a gigapixel-sized image of a whole pathology slide, our goal was to automatically detect and grade OOF regions at an accuracy level matching that of a pathologist across tissue, biopsy and stain types. To that end, we employed a convolutional neural network approach, which has shown superior performance over more classical machine learning approaches using hand-crafted features [9] . We term our approach ConvFocus (Figures 2-3).…”

Section: Methodsmentioning

confidence: 99%

“…[7] . Newer approaches have used deep convolutional neural networks [8] , which automatically learn the discriminative features and yield higher accuracies [9] [10] . The main challenge, particularly for the neural networks approaches, is the availability of training data to help generalization across a large variety of tissue morphologies and stain properties.…”

Section: Introductionmentioning

confidence: 99%

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Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Kohlberger

Liu

Moran

et al. 2019

Journal of Pathology Informatics

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Background: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected upon careful review, potentially causing rescanning and workflow delays. Although scan-time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical. Methods: We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process, and evaluated using real whole-slide images spanning 3 different tissue types and 3 different stain types that were digitized by two different scanners. ConvFocus's predictions were compared with pathologist-annotated focus quality grades across 514 distinct regions representing 37,700 35x35μm image patches, and 21 digitized "z-stack" whole-slide images that contain known OOF patterns. Results: When compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank coefficients of 0.81 and 0.94 on two scanners, and reproduced the expected OOF patterns from z-stack scanning. We also evaluated the impact of OOF on the accuracy of a state-of-the-art metastatic breast cancer detector and saw a consistent decrease in performance with increasing OOF. Conclusions: Comprehensive whole-slide OOF categorization could enable rescans prior to pathologist review, potentially reducing the impact of digitization focus issues on the clinical workflow. We show that the algorithm trained on our semi-synthetic OOF data generalizes well to real OOF regions across tissue types, stains, and scanners. Finally, quantitative OOF maps can flag regions that might otherwise be misclassified by image analysis algorithms, preventing OOF-induced errors.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Kohlberger

Liu

Moran

et al. 2019

Journal of Pathology Informatics

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“…To meet the needs for efficiency and fast computational speed, these methods employ statistical measurements for feature extraction that are related to absolute image blurriness. For detailed review of these methods please refer to [19]- [26] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Hosseini

Brawley-Hayes

Zhang

et al. 2020

IEEE Trans. Med. Imaging

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One of the challenges facing the adoption of digital pathology workflows for clinical use is the need for automated quality control. As the scanners sometimes determine focus inaccurately, the resultant image blur deteriorates the scanned slide to the point of being unusable. Also, the scanned slide images tend to be extremely large when scanned at greater or equal 20X image resolution. Hence, for digital pathology to be clinically useful, it is necessary to use computational tools to quickly and accurately quantify the image focus quality and determine whether an image needs to be re-scanned. We propose a no-reference focus quality assessment metric specifically for digital pathology images, that operates by using a sum of evenderivative filter bases to synthesize a human visual system-like kernel, which is modeled as the inverse of the lens' point spread function. This kernel is then applied to a digital pathology image to modify high-frequency image information deteriorated by the scanner's optics and quantify the focus quality at the patch level. We show in several experiments that our method correlates better with ground-truth z-level data than other methods, and is more computationally efficient. We also extend our method to generate a local slide-level focus quality heatmap, which can be used for automated slide quality control, and demonstrate the utility of our method for clinical scan quality control by comparison with subjective slide quality scores.Index Terms-no-reference focus quality assessment, whole slide imaging, digital pathology, out-of-focus heatmap, human visual system, MaxPol derivative library Routine diagnosis of pathology slides requires high quality highthroughput images, which are directly affected by the dynamic environment of the physical optics and sensor electronics of the scanner [2]-[4].

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“…The automation of the acquisition, especially through the use of whole slide imaging [6] and high-throughput microscopic equipment, has been instrumental in the development field. Labs can now acquire tens of thousands of data sets that can easily exceed gigabytes of data every month [7]. Previous work has applied computer-based image analysis for cell detection and classification [8], tissue classification [9], nuclei and mitosis detection [10], microvessel segmentation [11] and other immunohistochemistry scoring tasks [12] in histopathological images.…”

Section: Introductionmentioning

confidence: 99%

Modelling the nuclear envelope ofHeLacells

Karabağ

Jones

Peddie

et al. 2018

Preprint

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This paper describes a framework for the automatic segmentation of the nuclear envelope of cancerous HeLa cells and the modelling of the volumetric shape against an ellipsoid. The framework is automatic and unsupervised and reported a Jaccard Similarity Index of 0.968 against a manual segmentation. The modelling of the surface provides a visual display of the variations, both smooth and rugged over the surface, and measurements can be extracted with the expectation that they can correlate with the biological characteristics of the cells.

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Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology

Cited by 47 publications

References 16 publications

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Modelling the nuclear envelope ofHeLacells

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