Anna Vilanova

4.4k total citations
132 papers, 2.8k citations indexed

About

Anna Vilanova is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Anna Vilanova has authored 132 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Radiology, Nuclear Medicine and Imaging, 47 papers in Computer Vision and Pattern Recognition and 28 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Anna Vilanova's work include Advanced Neuroimaging Techniques and Applications (46 papers), Computer Graphics and Visualization Techniques (28 papers) and Advanced MRI Techniques and Applications (25 papers). Anna Vilanova is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (46 papers), Computer Graphics and Visualization Techniques (28 papers) and Advanced MRI Techniques and Applications (25 papers). Anna Vilanova collaborates with scholars based in Netherlands, Germany and Austria. Anna Vilanova's co-authors include Elmar Eisemann, Thomas Höllt, Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Bart M. ter Haar Romeny, Jarke J. van Wijk, Frans Gerritsen, Vincent van Unen, Frits Koning and Gustav J. Strijkers and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and The Journal of Experimental Medicine.

In The Last Decade

Anna Vilanova

126 papers receiving 2.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Anna Vilanova Netherlands 28 930 870 339 327 325 132 2.8k
Shin Nakajima Japan 19 1.1k 1.2× 1.3k 1.5× 137 0.4× 122 0.4× 182 0.6× 97 3.3k
Horst K. Hahn Germany 31 1.2k 1.3× 796 0.9× 188 0.6× 151 0.5× 421 1.3× 182 3.0k
Ron Kikinis United States 29 1.8k 1.9× 2.3k 2.6× 158 0.5× 143 0.4× 352 1.1× 84 5.2k
Danny Z. Chen United States 34 836 0.9× 1.1k 1.3× 385 1.1× 470 1.4× 683 2.1× 292 4.2k
Robert Pless United States 32 162 0.2× 1.6k 1.8× 262 0.8× 86 0.3× 333 1.0× 125 5.1k
Miles N. Wernick United States 33 2.1k 2.2× 829 1.0× 258 0.8× 78 0.2× 570 1.8× 199 3.9k
Grégoire Malandain France 36 1.6k 1.8× 2.3k 2.6× 564 1.7× 157 0.5× 377 1.2× 138 4.9k
J. Ross Mitchell Canada 28 1.1k 1.2× 339 0.4× 225 0.7× 37 0.1× 168 0.5× 71 2.4k
Boudewijn P. F. Lelieveldt Netherlands 40 2.0k 2.1× 1.9k 2.1× 1.1k 3.4× 42 0.1× 670 2.1× 216 6.4k
Hideo Yokota Japan 24 596 0.6× 309 0.4× 845 2.5× 50 0.2× 123 0.4× 175 3.0k

Countries citing papers authored by Anna Vilanova

Since Specialization
Citations

This map shows the geographic impact of Anna Vilanova'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 Anna Vilanova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna Vilanova more than expected).

Fields of papers citing papers by Anna Vilanova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna Vilanova. 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 Anna Vilanova. The network helps show where Anna Vilanova may publish in the future.

Co-authorship network of co-authors of Anna Vilanova

This figure shows the co-authorship network connecting the top 25 collaborators of Anna Vilanova. A scholar is included among the top collaborators of Anna Vilanova 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 Anna Vilanova. Anna Vilanova is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Ruijters, Daniël, et al.. (2025). NeRF-CA: Dynamic Reconstruction of X-Ray Coronary Angiography With Extremely Sparse-Views. IEEE Transactions on Visualization and Computer Graphics. 31(10). 8782–8795.
3.
Staring, Marius, et al.. (2024). Inclusion Depth for Contour Ensembles. IEEE Transactions on Visualization and Computer Graphics. 30(9). 6560–6571. 1 indexed citations
4.
Hildebrandt, Klaus, Marius Staring, Eleftheria Astreinidou, et al.. (2024). Implementation of delineation error detection systems in time-critical radiotherapy: Do AI-supported optimization and human preferences meet?. Cognition Technology & Work. 27(1-2). 41–57. 1 indexed citations
5.
L’Yi, Sehi, et al.. (2024). Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks. IEEE Transactions on Visualization and Computer Graphics. 31(1). 1180–1190. 3 indexed citations
6.
Molenaar, M., Marius Staring, René van Egmond, et al.. (2024). Depth for Multi‐Modal Contour Ensembles. Computer Graphics Forum. 43(3).
7.
Gilst, Merel M. van, et al.. (2023). FlexEvent: going beyond Case‐Centric Exploration and Analysis of Multivariate Event Sequences. Computer Graphics Forum. 42(3). 161–172. 2 indexed citations
8.
Lew, Baldur van, Jeroen Eggermont, Elmar Eisemann, et al.. (2023). ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics. 30(1). 1–11. 2 indexed citations
9.
Eisemann, Elmar, et al.. (2022). ComVis‐Sail: Comparative Sailing Performance Visualization for Coaching. Computer Graphics Forum. 42(1). 86–100.
10.
Sloun, Ruud J. G. van, et al.. (2022). The Transform-and-Perform Framework: Explainable Deep Learning Beyond Classification. IEEE Transactions on Visualization and Computer Graphics. 30(2). 1502–1515. 4 indexed citations
11.
Elzen, Stef van den, et al.. (2022). ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection. Computer Graphics Forum. 41(3). 97–108. 10 indexed citations
12.
Smit, Noeska, et al.. (2021). Ten Open Challenges in Medical Visualization. IEEE Computer Graphics and Applications. 41(5). 7–15. 20 indexed citations
13.
Höllt, Thomas, et al.. (2021). A Progressive Approach for Uncertainty Visualization in Diffusion Tensor Imaging. Computer Graphics Forum. 40(3). 411–422. 8 indexed citations
14.
Jalba, Andrei C., et al.. (2020). Data Assimilation for Full 4D PC‐MRI Measurements: Physics‐Based Denoising and Interpolation. Computer Graphics Forum. 39(6). 496–512. 5 indexed citations
15.
Pezzotti, Nicola, Alexander Mordvintsev, Thomas Höllt, et al.. (2018). Linear tSNE optimization for the Web.. arXiv (Cornell University). 4 indexed citations
16.
Schultz, Thomas & Anna Vilanova. (2018). Diffusion MRI visualization. NMR in Biomedicine. 32(4). e3902–e3902. 11 indexed citations
17.
Boonkkamp, J. H. M. ten Thije, et al.. (2015). Diffusion tensor imaging : brain pathway reconstruction. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 45(4). 259–265. 1 indexed citations
18.
Jalba, Andrei C., et al.. (2011). CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking. International Journal of Biomedical Imaging. 2011. 1–12. 11 indexed citations
19.
Vilanova, Anna, et al.. (2010). An innovative geodesic based multi-valued fiber-tracking algorithm for diffusion tensor imaging. TU/e Research Portal (Eindhoven University of Technology). 1027. 3 indexed citations
20.
Vilanova, Anna, et al.. (1999). VirEn : A virtual endoscopy system. 469–487. 17 indexed citations

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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