Immersive Anatomical Scenes that Enable Multiple Users to Occupy the Same Virtual Space: A Tool for Surgical Planning and Education
November 05, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Alex J. Deakyne, Erik N. Gaasedelen, Tinen L. Iles, Paul A. Iaizzo
arXiv ID
2012.02596
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.GR
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
3D modeling is becoming a well-developed field of medicine, but its applicability can be limited due to the lack of software allowing for easy utilizations of generated 3D visualizations. By leveraging recent advances in virtual reality, we can rapidly create immersive anatomical scenes as well as allow multiple users to occupy the same virtual space: i.e., over a local or distributed network. This setup is ideal for pre-surgical planning and education, allowing users to identify and study structures of interest. I demonstrate here such a pipeline on a broad spectrum of anatomical models and discuss its applicability to the medical field and its future prospects.3D modeling is becoming a well-developed field of medicine, but its applicability can be limited due to the lack of software allowing for easy utilizations of generated 3D visualizations. By leveraging recent advances in virtual reality, we can rapidly create immersive anatomical scenes as well as allow multiple users to occupy the same virtual space: i.e., over a local or distributed network. This setup is ideal for pre-surgical planning and education, allowing users to identify and study structures of interest. I demonstrate here such a pipeline on a broad spectrum of anatomical models and discuss its applicability to the medical field and its future prospects.
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