Demo Data from VLDB 2026 / reviewers in the wild / expert
Chetan Gudisagar
dblp:213/0368
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 50% Rendering · 50% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › neuroscience
neuroinformatics |
0.6 | 1 | 2022 | NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022 |
Rendering
hybrid rendering |
0.2 | 1 | 2022 | NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022 |
Visualization and visual analytics
volume visualization |
0.2 | 1 | 2022 | NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022 |
Methods — techniques the papers use, named apart from their topics
image registration · 1.1convolutional neural network · 1.1isosurface rendering · 0.6iso-surface rendering · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain ImagesabstractWe introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease. Parmida Ghahremani, Saeed Boorboor, Pooya Mirhosseini, Chetan Gudisagar, Mala Ananth 0001, David Talmage, Lorna Role, Arie E. Kaufman |
IEEE Trans. Vis. Comput. Graph. | 4 |