Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data

Jessup, J. M.; Krueger, Robert F.; Warchol, Simon; Hoffer, John; Muhlich, Jeremy; Ritch, Cecily C.; Gaglia, Giorgio; Coy, Shannon; Chen, Yuan; Lin, Jia-Ren; Santagata, Sandro; Sorger, Peter K.; Pfister, Hanspeter

doi:10.1109/tvcg.2021.3114786

Cited by 15 publications

(16 citation statements)

References 54 publications

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“…A wide variety of bioimaging data viewers (e.g., OMERO Pathviewer [35], ViV [53]), Napari [82], Cytomine [75], Minerva [70]) and visual analysis tools (e.g., ParaGlyder [57], Vitessce [19], Facetto [40], Scope2Screen [32]) are used to study multiplexed tissue images and derived feature data. Visualization and analysis methods for older spatially resolved modalities, by contrast, often operate directly on the pixel data, though not at the single-cell resolution [6,7,13,17].…”

Section: Related Work 21 Visual Spatial Analysis Of Biomedical Imagin...mentioning

confidence: 99%

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Warchol

Krueger

Nirmal

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

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Section: Related Work 21 Visual Spatial Analysis Of Biomedical Imagin...mentioning

confidence: 99%

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Warchol

Krueger

Nirmal

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…Advances in translational and clinical research build on fundamental cancer biology and basic research. Researchers in the Human Tumor Atlas Network (HTAN) have constructed three-dimensional maps of human cancers and created a new, powerful imaging technique that can visualize the cell type and spatial arrangements at the single-cell and tissue levels 18 .…”

Section: Supporting Basic Sciencementioning

confidence: 99%

A new phase of the Cancer Moonshot to end cancer as we know it

Singer

2022

Nat Med

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“…3, d), we offer a scalable image viewer (Fig 1, a), allowing navigation via zoom-ing+panning. The viewer builds on our previous work Facetto [31] and Scope2Screen [24]. Through multi-channel rendering, pseudo-colored channels can be blended together into a single view, enabling users to analyze the expression level of multiple markers at once.…”

Section: Visualizing Spatial Neighborhoodsmentioning

confidence: 99%

“…A wide variety of bioimaging data viewers (e.g., OMERO Pathviewer [27], ViV [43]), Napari [68], Cytomine [61], Minerva [56]) and visual analysis tools (e.g., ParaGlyder [47], Vitessce [13], Facetto [31], Scope2Screen [24]) are used to study multiplexed tissue images and derived feature data. Generally, these systems focus on visual exploration and cell-type identification but they are not intended to analyze interactions between cell types and larger spatial neighborhoods that tissue microenvironments are composed of.…”

Section: Related Work 21 Visual Spatial Analysis Of Biomedical Imagin...mentioning

confidence: 99%

“…We feature a JavaScript client / Python server architecture with web-based frontend visualization and efficient backend computation. We built on our previous work (Scope2Screen [24], Minerva [21, 56]), storing images in the Zarr [45] format and rendering them using WebGL. The embedding view, linked image thumbnails, and spatial comparison views use the regl-scatterplot [34] library, which allows for panning, zooming, and selection in datasets containing as many as 20 million points.…”

Section: Scalable Implementationmentioning

confidence: 99%

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Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Warchol¹,

Krueger

Nirmal

et al. 2022

Preprint

Self Cite

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ThiNew multiplexed tissue imaging technologies have enabled the study of normal and diseased tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood networks and immune-tumor interactions.

show abstract

Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data

Cited by 15 publications

References 54 publications

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

A new phase of the Cancer Moonshot to end cancer as we know it

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

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