EDBT 2026 Demo / reviewers in the wild / expert
Chesnal D. Arepalli
dblp:135/1042
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Medical and health informatics · 100% | |
| Artificial intelligence
1 paper |
Segmentation and scene understanding · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding
medical image segmentation |
0.2 | 1 | 2014 | A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014 |
Medical and health informatics › medical imaging › medical image analysis
cardiac image segmentation |
0.2 | 1 | 2014 | A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014 |
Medical and health informatics › medical imaging
medical image analysis |
0.2 | 1 | 2014 | A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution · IEEE Trans. Image Process. 2014 |
Methods — techniques the papers use, named apart from their topics
shape segmentation · 0.4contour evolution · 0.4coarse-to-fine strategy · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour EvolutionabstractThe left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated. Liangjia Zhu, Yi Gao 0002, Vikram V. Appia, Anthony J. Yezzi, Chesnal D. Arepalli, Tracy L. Faber, Arthur E. Stillman, Allen R. Tannenbaum |
IEEE Trans. Image Process. | 5 |