Vladimír Pekar

2.0k total citations · 1 hit paper
37 papers, 1.3k citations indexed

About

Vladimír Pekar is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Radiation. According to data from OpenAlex, Vladimír Pekar has authored 37 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 15 papers in Biomedical Engineering and 12 papers in Radiation. Recurrent topics in Vladimír Pekar's work include Medical Image Segmentation Techniques (22 papers), Advanced Radiotherapy Techniques (12 papers) and Medical Imaging and Analysis (12 papers). Vladimír Pekar is often cited by papers focused on Medical Image Segmentation Techniques (22 papers), Advanced Radiotherapy Techniques (12 papers) and Medical Imaging and Analysis (12 papers). Vladimír Pekar collaborates with scholars based in Germany, Finland and Canada. Vladimír Pekar's co-authors include Michael R. Kaus, David A. Jaffray, Jürgen Weese, Todd McNutt, Nadya Shusharina, G Sharp, Karl Fritscher, Jinzhong Yang, Harini Veeraraghavan and M. Peroni and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Magnetic Resonance in Medicine and IEEE Transactions on Medical Imaging.

In The Last Decade

Vladimír Pekar

34 papers receiving 1.3k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Vision 20/20: Perspectives on automated image segmentation for radiotherapy

2014 284 citations G Sharp, Karl Fritscher et al. Medical Physics profile →

Peers

Countries citing papers authored by Vladimír Pekar

Since Specialization

Citations

This map shows the geographic impact of Vladimír Pekar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vladimír Pekar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimír Pekar more than expected).

Fields of papers citing papers by Vladimír Pekar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vladimír Pekar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vladimír Pekar. The network helps show where Vladimír Pekar may publish in the future.

Co-authorship network of co-authors of Vladimír Pekar

This figure shows the co-authorship network connecting the top 25 collaborators of Vladimír Pekar. A scholar is included among the top collaborators of Vladimír Pekar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vladimír Pekar. Vladimír Pekar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Temporal dynamics of lymphocytes in prostate cancer patients treated with proton therapy 2025 ·Frontiers in Oncology ·(unknown), (unknown), Vladimír Vondráček, Vladimír Pekar, (unknown), (unknown), Iva Benešová, (unknown), (unknown), (unknown), Pavel Vítek, Jiří Kubeš	0
2	Automatic learning‐based beam angle selection for thoracic IMRT 2015 ·Medical Physics ·(unknown), Thomas G. Purdie, Alex Levinshtein, Andrew Hope, Patricia Lindsay, Andrea Marshall, David A. Jaffray, Vladimír Pekar	24
3	Vision 20/20: Perspectives on automated image segmentation for radiotherapy breakdown → 2014 ·Medical Physics ·G Sharp, Karl Fritscher, Vladimír Pekar, M. Peroni, Nadya Shusharina, Harini Veeraraghavan, Jinzhong Yang	284
4	A feature-based approach for refinement of Model-based segmentation of low contrast structures 2011 ·PubMed ·(unknown), John Kim, David A. Jaffray, Vladimír Pekar	2
5	Auto‐segmentation of normal and target structures in head and neck CT images: A feature‐driven model‐based approach 2011 ·Medical Physics ·(unknown), Vladimír Pekar, John Kim, Jason Xie, Stephen Breen, David A. Jaffray	96
6	Detection of point landmarks in 3D medical images via phase congruency model 2011 ·Journal of the Brazilian Computer Society ·Ricardo J. Ferrari, Stéphane Allaire, Andrew Hope, John Kim, David A. Jaffray, Vladimír Pekar	5
7	Towards automatic patient positioning and scan planning using continuously moving table MR imaging 2009 ·Magnetic Resonance in Medicine ·Peter Koken, Sebastian P. M. Dries, Jochen Keupp, (unknown), Vladimír Pekar, Peter Börnert	2
8	Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region 2008 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·(unknown), T. Vik, Heinrich Schulz, Stéphane Allaire, John Kim, Laura A. Dawson, Brian O’Sullivan, Stephen Breen, David A. Jaffray, Vladimír Pekar	7
9	Automatic Contour Propagation in Head & Neck IGRT Based on 3D Salient Interest Points 2008 ·International Journal of Radiation OncologyBiologyPhysics ·Stéphane Allaire, Vladimír Pekar, Andrew Hope, Stephen Breen, David A. Jaffray	4
10	Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning 2007 ·International Journal of Radiation OncologyBiologyPhysics ·Michael R. Kaus, Kristy K. Brock, Vladimír Pekar, Laura A. Dawson, Alan Nichol, David A. Jaffray	126
11	Spine Detection and Labeling Using a Parts-Based Graphical Model 2007 ·Lecture notes in computer science ·Stefan Schmidt, Jörg Hendrik Kappes, Martin Bergtholdt, Vladimír Pekar, Sebastian P. M. Dries, (unknown), Christoph Schnörr	90
12	Automated Planning of Scan Geometries in Spine MRI Scans 2007 ·Lecture notes in computer science ·Vladimír Pekar, (unknown), (unknown), Sebastian P. M. Dries, Stefan Schmidt, (unknown), (unknown), (unknown), Arjan W. Simonetti, (unknown)	18
13	An adaptive irregular grid approach for 3D deformable image registration 2006 ·Physics in Medicine and Biology ·Vladimír Pekar, Evgeny Gladilin, Karl Rohr	25
14	Automated model-based organ delineation for radiotherapy planning in prostatic region 2004 ·International Journal of Radiation OncologyBiologyPhysics ·Vladimír Pekar, Todd McNutt, Michael R. Kaus	121
15	Segmentation-aided adaptive filtering for metal artifact reduction in radio-therapeutic CT images 2004 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·(unknown), Michael R. Kaus, Vladimír Pekar, (unknown), Lothar Spies	19
16	Automated segmentation of the left ventricle in cardiac MRI 2004 ·Medical Image Analysis ·Michael R. Kaus, Jens von Berg, Jürgen Weese, Wiro J. Niessen, Vladimír Pekar	182
17	Automated 3-D PDM construction from segmented images using deformable models 2003 ·IEEE Transactions on Medical Imaging ·Michael R. Kaus, Vladimír Pekar, (unknown), Roel Truyen, S. Lobregt, Jürgen Weese	86
18	Automated 3D PDM construction using deformable models 2002 ·Michael R. Kaus, Vladimír Pekar, (unknown), Roel Truyen, S. Lobregt, (unknown), Jürgen Weese	6
19	Fast detection of meaningful isosurfaces for volume data visualization 2001 ·IEEE Visualization ·Vladimír Pekar, Rafael Wiemker, (unknown)	45
20	<title>Shape-model-based adaptation of 3D deformable meshes for segmentation of medical images</title> 2001 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Vladimír Pekar, Michael R. Kaus, Cristian Lorenz, S. Lobregt, Roel Truyen, Juergen Weese	6

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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