Segmentation and localisation of whole slide images using unsupervised learning

Hiary, Hazem; Alomari, Raja’ S.; Chaudhary, Vipin

doi:10.1049/iet-ipr.2013.0008

Cited by 12 publications

(20 citation statements)

References 32 publications

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“…Neither the 8 images nor their tissue-staining categories were used for training or validation of the Full results are presented in Table 7. We did not include the published tissue segmentation method by Hiary et al (Hiary et al, 2013) in this study: They reported their results as a localization error in which a pathologist partly determined which errors were relevant (and thus counted), making the approach irreproducible for us. Re-implementing this method was not feasible due to missing algorithmic details.…”

Section: Results On the Dissimilar Datasetmentioning

confidence: 99%

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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.

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Section: Results On the Dissimilar Datasetmentioning

confidence: 99%

“…Manuscript to be reviewed et al, 2015); the result is subsequently refined via flood filling from identified background points. Hiary et al built a different algorithm based on k-means clustering using pixel intensity, color, and texture features (Hiary et al, 2013).…”

Section: Introductionmentioning

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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“…We did not directly compare to the other published tissue segmentation methods by Hiary et al [3]. They report their results as a localization error in which a pathologist partly determined which errors were relevant (and thus counted), making the approach irreproducible for us.…”

Section: Discussionmentioning

confidence: 98%

Comparison of different methods for tissue segmentation in histopathological whole-slide images

Bándi

Loo

Intezar

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).

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“…Therefore, there is no single segmentation method with acceptable results for all medical imaging modalities [ 9 ]. According to that challenge, medical imaging segmentation remains a problem for this field [ 11 ]. There are different approaches in medical image segmentation with some approaches based on heuristics, region growing, edge detection, and thresholding methods [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

“…The problem of localizing the focus point regions from the whole-slide tissue image can be addressed as a clustering problem in image processing [ 9 , 11 – 13 ]. Our objective in this paper is to propose an adaptive localization method for Ki-67 staining of whole-slide tissue for histology images of a brain tumor.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

Alomari

Abdullah

MdZin

et al. 2015

Computational and Mathematical Methods in Medicine

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Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

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Segmentation and localisation of whole slide images using unsupervised learning

Cited by 12 publications

References 32 publications

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

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

Comparison of different methods for tissue segmentation in histopathological whole-slide images

Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

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