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Chetan Gudisagar

dblp:213/0368 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › neuroscience
neuroinformatics
0.612022
NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022
Rendering
hybrid rendering
0.212022
NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022
Visualization and visual analytics
volume visualization
0.212022
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
YearPublicationVenuePosition
2022 NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images
abstract
We 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