Yun Stone Shi

dblp:388/9936 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
biomedical visualization
0.812024
Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › biological data visualization
neuron tracing
0.812024
Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024
Immersive interaction › extended reality
extended reality interaction
0.812024
Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics
volume visualization
0.212024
Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

interactors · 1.5extended reality · 1.5GPU-accelerated tracing · 1.5
YearPublicationVenuePosition
2024 Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality
abstract
Neuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving accurate neuron tracing is critical for profiling the neuroanatomical structure at single-cell level and analyzing the neuronal circuits and projections at whole-brain scale. However, the process often demands substantial human involvement and represents a nontrivial task. Conventional solutions towards neuron tracing often contend with challenges such as non-intuitive user interactions, suboptimal data generation throughput, and ambiguous visualization. In this paper, we introduce a novel method that leverages the power of extended reality (XR) for intuitive and progressive semi-automatic neuron tracing in real time. In our method, we have defined a set of interactors for controllable and efficient interactions for neuron tracing in an immersive environment. We have also developed a GPU-accelerated automatic tracing algorithm that can generate updated neuron reconstruction in real time. In addition, we have built a visualizer for fast and improved visual experience, particularly when working with both volumetric images and 3D objects. Our method has been successfully implemented with one virtual reality (VR) headset and one augmented reality (AR) headset with satisfying results achieved. We also conducted two user studies and proved the effectiveness of the interactors and the efficiency of our method in comparison with other approaches for neuron tracing.
Zexin Yuan, Jiaqi Xi, Ziqin Gao, Ying Li 0028, Xiaoqiang Zhu, Yun Stone Shi, Frank Guan
IEEE Trans. Vis. Comput. Graph.7