EDBT 2026 Demo / reviewers in the wild / expert
Arthur E. Stillman
dblp:119/0644
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2014
0000-0002-7911-1920ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2Graphics, 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. | 7 |
| 2012 | A System for Seismocardiography-Based Identification of Quiescent Heart Phases: Implications for Cardiac ImagingabstractSeismocardiography (SCG), a representation of mechanical heart motion, may more accurately determine periods of cardiac quiescence within a cardiac cycle than the electrically derived electrocardiogram (EKG) and, thus, may have implications for gating in cardiac computed tomography. We designed and implemented a system to synchronously acquire echocardiography, EKG, and SCG data. The device was used to study the variability between EKG and SCG and characterize the relationship between the mechanical and electrical activity of the heart. For each cardiac cycle, the feature of the SCG indicating Aortic Valve Closure was identified and its time position with respect to the EKG was observed. This position was found to vary for different heart rates and between two human subjects. A color map showing the magnitude of the SCG acceleration and computed velocity was derived, allowing for direct visualization of quiescent phases of the cardiac cycle with respect to heart rate. Carson A. Wick, Jin-Jyh Su, James H. McClellan, Oliver Brand, Pamela T. Bhatti, Ashley L. Buice, Arthur E. Stillman, Xiangyang Tang, Srini Tridandapani |
IEEE Trans. Inf. Technol. Biomed. | 7 |
| 2006 | Coronary vessel trees from 3D imagery: A topological approach
Andrzej Szymczak, Arthur E. Stillman, Allen R. Tannenbaum, Konstantin Mischaikow |
Medical Image Anal. | 2 |