Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Lee, Sanghoon; Amgad, Mohamed; Mobadersany, Pooya; McCormick, Matt; Pollack, Brian P.; Elfandy, Habiba; Hussein, Hagar; Gutman, David; Cooper, Lee

doi:10.1158/0008-5472.can-20-0668

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…lf the user prefers to annotate locally, we have added options to ingest and export annotations in an Extensible Markup Language ( XML) 24 format readable by the commonly used WSI viewer Aperio ImageScope 25 . The authors note two complimentary works: HistomicsML 26 and Quick Annotator 27 , both use superpixels 28 and active learning 29 to speed the annotation process. HistomicsML also uses HistomicsUI for deployment, and Quick Annotator is run locally in the QuPath slide viewer 30 .…”

Section: Discussionmentioning

confidence: 99%

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

Lutnick

Manthey

Becker

et al. 2021

Preprint

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Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

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Section: Discussionmentioning

confidence: 99%

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

Lutnick

Manthey

Becker

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…If the user prefers to annotate locally, we have added options to ingest and export annotations in an Extensible Markup Language (XML) 24 format readable by the commonly used WSI viewer Aperio ImageScope 25 . The authors note two complimentary works: HistomicsML 26 and Quick…”

Section: Discussionmentioning

confidence: 99%

A User-Friendly Tool For Cloud-Based Whole Slide Image Segmentation, With Examples From Renal Histopathology

Lutnick¹,

Manthey

Becker

et al. 2021

Preprint

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We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain.

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“…A limitation of this approach is the requirement for on-going support for personnel; on-going support is critical to long term success. To ease the generation of high-quality training data with a “human-in-the-loop” approach, methods have also been established around segmentation refinement ( Sullivan et al, 2018 ; Lutnick et al, 2019 ; Moen et al, 2019 ; Govind et al, 2021 ; Lee et al, 2021 ). An alternative to these brute-force approach has been to generate synthetic training data by combining “blob” models of cells with real images using generative adversarial networks ( Dunn et al, 2019 ; Wu et al, 2021 ).…”

Section: Segmentationmentioning

confidence: 99%

User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue

Winfree

2022

Front. Physiol.

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Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms. These approaches have been used for the analysis of cells in situ, within tissue, and confirmed existing and uncovered new models of cellular microenvironments in human disease. This has been achieved by the development of both imaging modality specific and multimodal solutions for cellular segmentation, thus addressing the fundamental requirement for high quality and reproducible cell segmentation in images from immunofluorescence, immunohistochemistry and histological stains. The expansive landscape of cell types-from a variety of species, organs and cellular states-has required a concerted effort to build libraries of annotated cells for training data and novel solutions for leveraging annotations across imaging modalities and in some cases led to questioning the requirement for single cell demarcation all together. Unfortunately, bleeding-edge approaches are often confined to a few experts with the necessary domain knowledge. However, freely available, and open-source tools and libraries of trained machine learning models have been made accessible to researchers in the biomedical sciences as software pipelines, plugins for open-source and free desktop and web-based software solutions. The future holds exciting possibilities with expanding machine learning models for segmentation via the brute-force addition of new training data or the implementation of novel network architectures, the use of machine and deep learning in cell and neighborhood classification for uncovering cellular microenvironments, and the development of new strategies for the use of machine and deep learning in biomedical research.

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Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Cited by 17 publications

References 18 publications

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

A User-Friendly Tool For Cloud-Based Whole Slide Image Segmentation, With Examples From Renal Histopathology

User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue

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