Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web

Manz, Trevor; Gold, Ilan; Patterson, Nathan Heath; McCallum, Chuck; Keller, Mark S; Herr, Bruce W.; Börner, Katy; Spraggins, Jeffrey M.; Gehlenborg, Nils

doi:10.1038/s41592-022-01482-7

Cited by 34 publications

(35 citation statements)

References 31 publications

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“…Several performant, webGL based, image viewers 7–10 can read next generation file formats. Most of them currently require setting up a dedicated server to access the image data (webKnossos 10 , Neuroglancer 9 , Catmaid 7 ), only Viv 8 currently runs purely on the client side and does not need a server. Of these tools, Neuroglancer is most similar to MoBIE, sharing several functionalities such as arbitrary slicing of 3D data, interactive rendering of segmentation data and a JSON based schema to specify the viewer state.…”

Section: Methodsmentioning

confidence: 99%

“…Further, to promote cross-tool accessibility, we are invested in the standardization of MoBIE specifications, for example, transformations, views and tables, through the OME-NGFF process. We envision that this integration will enable other tools, for example Neuroglancer 9 webKnossos 10 and Viv 8 , to access the same data and views as MoBIE.…”

Section: Mainmentioning

confidence: 99%

“…MoBIE is implemented using existing tools wherever possible, in particular BigDataViewer 5 (BDV) and the ImgLib2 6 and N5-java 4 libraries. An overview of other big image data visualization tools [7][8][9][10] and their relation to MoBIE is given in Methods.…”

mentioning

confidence: 99%

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MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

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Meechan

Moreva

et al. 2022

Preprint

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Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.

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Section: Methodsmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

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MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

Pape

Meechan

Moreva

et al. 2022

Preprint

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“…To study the biodistribution of radiolabeled NPs, positron emission tomography (PET) and/or computed tomography is used with an arsenal of software for image reconstruction and processing, such as Tera-Tomo 3D PET, Nucline, and VivoQuant [ 85 ]. In addition, a number of other software platforms can be used for quantitative analysis of microscopic images, including CellTracker [ 166 ], spatialTIME and iTIME [ 167 ], DeepImageJ [ 168 ], MCMICRO [ 169 ], Viv [ 170 ], and others. A more detailed description, comparison, and analysis of these platforms may be the subject of future works.…”

Section: Limitation Of the Reviewmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova

Akberdin

Kiselev

et al. 2022

IJMS

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Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

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“…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%

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Warchol¹,

Krueger

Nirmal

et al. 2022

Preprint

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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.

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Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web

Cited by 34 publications

References 31 publications

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

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