Vitessce Link: A Mixed Reality and 2D Display Hybrid Approach for Visual Analysis of 3D Tissue Maps
November 06, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Eric MΓΆrth, Morgan L. Turner, Cydney Nielsen, Xianhao Carton Liu, Mark Keller, Lisa Choy, John Conroy, Tabassum Kakar, Clarence Yapp, Alex Wong, Peter Sorger, Liam McLaughlin, Sanjay Jain, Johanna Beyer, Hanspeter Pfister, Chen Zhu-Tian, Nils Gehlenborg
arXiv ID
2511.04262
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Advances in spatial omics and high-resolution imaging enable the creation of three-dimensional (3D) tissue maps that capture cellular organization and interactions in situ. While these data provide critical insights into tissue function and disease, their exploration is often constrained by tools limited to 2D displays or stereoscopic rendering without analytical integration. We present Vitessce Link, a web-based hybrid framework that unites a 3D stereoscopic view in mixed reality with a synchronized 2D display environment. Users can navigate volumetric data with intuitive hand gestures while controlling channels, filters, and derived data views through the Vitessce platform. Built on open standards and running entirely in the browser, Vitessce Link minimizes friction, supports integration with computational notebooks, and synchronizes interactions across devices via a lightweight WebSocket architecture. Case studies in nephrology and oncology demonstrate how the hybrid approach enhances segmentation evaluation, distance measurement, and interpretation of spatial relationships. Vitessce Link establishes a paradigm for integrative, web-native analysis of 3D tissue maps.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted