Segmentation methods of H&amp;E-stained histological images of lymphoma: A review

Tosta, Tháına A. A.; Neves, Leandro Alves; Nascimento, Marcelo Zanchetta do

doi:10.1016/j.imu.2017.05.009

Cited by 43 publications

(26 citation statements)

References 35 publications

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“…It calculates the optimum threshold separating the two classes so that their combined intra-class variance is minimal. It has been widely used in image analysis applications and digital histopathology (Bándi et al, 2018; Azevedo Tosta, Neves & do Nascimento, 2017; Campanella et al, 2019; Nirschl et al, 2018; Xu, Park & Hwang, 2019; Vanderbeck et al, 2014). For applying Otsu’s method the WSIs were first converted to grayscale by averaging the red, green, and blue channels.…”

Section: Methodsmentioning

confidence: 99%

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Bándi

Balkenhol

Ginneken

et al. 2019

PeerJ

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Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions.In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples—staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to three traditional methods on a range of resolution levels using Dice score and sensitivity.We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the eight unfamiliar images. A future study should test this network prospectively.

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Section: Methodsmentioning

confidence: 99%

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Bándi

Balkenhol

Ginneken

et al. 2019

PeerJ

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“…Combining immunohistochemical data with digital imaging is definitely the way to go in the future. The exploitation of digital images in cancer diagnosis [ 18 , 19 , 20 ], for example, optimized by image segmentation methods capable of detection ROI automatically prior to further classification by SOM-QE, can provide objective analyses that make the difficult task of diagnosis more reliable and less time consuming for the human expert. Finally, in the current context of pandemic explosion of SARS-CoV, a particular class of coronavirus, there is hope that cellular imaging models, like the one described in [ 7 ], which inspired this study here, that account for in vitro coronavirus entry and proliferation mechanisms in cell lines or other cultured cells will allow for a better understanding of the infection process [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data

Dresp-Langley¹,

Wandeto

2020

Informatics in Medicine Unlocked

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Cellular and molecular imaging techniques and models have been developed to characterize single stages of viral proliferation after focal infection of cells in vitro . The fast and automatic classification of cell imaging data may prove helpful prior to any further comparison of representative experimental data to mathematical models of viral propagation in host cells. Here, we use computer generated images drawn from a reproduction of an imaging model from a previously published study of experimentally obtained cell imaging data representing progressive viral particle proliferation in host cell monolayers. Inspired by experimental time-based imaging data, here in this study viral particle increase in time is simulated by a one-by-one increase, across images, in black or gray single pixels representing dead or partially infected cells, and hypothetical remission by a one-by-one increase in white pixels coding for living cells in the original image model. The image simulations are submitted to unsupervised learning by a Self-Organizing Map (SOM) and the Quantization Error in the SOM output (SOM-QE) is used for automatic classification of the image simulations as a function of the represented extent of viral particle proliferation or cell recovery. Unsupervised classification by SOM-QE of 160 model images, each with more than three million pixels, is shown to provide a statistically reliable, pixel precise, and fast classification model that outperforms human computer-assisted image classification by RGB image mean computation. The automatic classification procedure proposed here provides a powerful approach to understand finely tuned mechanisms in the infection and proliferation of virus in cell lines in vitro or other cells.

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“…It calculates the optimum threshold separating the two classes so that their combined intra-class variance is minimal. It has been widely used in image analysis applications and digital histopathology (Bándi et al, 2018;Azevedo Tosta et al, 2017;Campanella et al, 2019;Nirschl et al, 2018;Xu et al, 2019;Vanderbeck et al, 2014). For applying Otsu's method the WSIs were first converted to grayscale by averaging the red, green, and blue channels.…”

Section: Otsu's Adaptive Thresholdmentioning

confidence: 99%

Peer Review #1 of "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks (v0.1)"

2019

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Modern pathology diagnostics is being driven towards large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples-staining categories from 5 medical centers for development and testing. Additionally, 8 more images of 8 unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to 3 traditional methods on a range of resolution levels using Dice score and sensitivity. We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the 8 unfamiliar images. A future study should test this network prospectively.

show abstract

Segmentation methods of H&E-stained histological images of lymphoma: A review

Cited by 43 publications

References 35 publications

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data

Peer Review #1 of "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks (v0.1)"

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