Benoît Presles

537 total citations
30 papers, 332 citations indexed

About

Benoît Presles is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Benoît Presles has authored 30 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Pulmonary and Respiratory Medicine and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Benoît Presles's work include Medical Imaging Techniques and Applications (7 papers), Advanced Radiotherapy Techniques (7 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Benoît Presles is often cited by papers focused on Medical Imaging Techniques and Applications (7 papers), Advanced Radiotherapy Techniques (7 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Benoît Presles collaborates with scholars based in France, United Kingdom and United States. Benoît Presles's co-authors include Johan Debayle, Marie‐Claude Biston, David Sarrut, Simon Rit, Alain Lalande, P. Pommier, Farhan Akram, Hatem A. Rashwan, Vivek Kumar Singh and Santiago Romaní and has published in prestigious journals such as Scientific Reports, IEEE Access and Acta Biomaterialia.

In The Last Decade

Benoît Presles

29 papers receiving 326 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benoît Presles France 11 142 91 84 74 73 30 332
Matteo Pepa Italy 9 155 1.1× 46 0.5× 73 0.9× 75 1.0× 109 1.5× 33 332
Jinghao Zhou United States 14 229 1.6× 186 2.0× 130 1.5× 105 1.4× 128 1.8× 33 456
Pascal Cathier France 11 270 1.9× 114 1.3× 26 0.3× 74 1.0× 104 1.4× 21 466
Fredrik Löfman Sweden 6 264 1.9× 31 0.3× 119 1.4× 81 1.1× 73 1.0× 9 422
Martin Segeroth Switzerland 5 337 2.4× 78 0.9× 45 0.5× 180 2.4× 84 1.2× 11 516
Sijuan Huang China 10 201 1.4× 79 0.9× 80 1.0× 78 1.1× 82 1.1× 45 371
Umair Javaid Belgium 6 149 1.0× 28 0.3× 40 0.5× 55 0.7× 50 0.7× 11 287
James I. Monroe United States 9 180 1.3× 36 0.4× 194 2.3× 77 1.0× 128 1.8× 23 321
Chengtao Peng China 13 205 1.4× 90 1.0× 13 0.2× 146 2.0× 31 0.4× 17 362

Countries citing papers authored by Benoît Presles

Since Specialization
Citations

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

Fields of papers citing papers by Benoît Presles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benoît Presles

This figure shows the co-authorship network connecting the top 25 collaborators of Benoît Presles. A scholar is included among the top collaborators of Benoît Presles 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 Benoît Presles. Benoît Presles 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
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Presles, Benoît, Sarah Leclerc, Olivier Chevallier, et al.. (2024). Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour segmentation. Scientific Reports. 14(1). 3522–3522. 5 indexed citations
3.
Presles, Benoît, Charles Coutant, Isabelle Desmoulins, et al.. (2024). Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer. EJNMMI Research. 14(1). 60–60. 3 indexed citations
4.
Salvadori, Julien, Benoît Presles, Jorge Cabello, et al.. (2024). PET digitization chain for Monte Carlo simulation in GATE. Physics in Medicine and Biology. 69(16). 165013–165013. 1 indexed citations
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Presles, Benoît, Sarah Leclerc, Fabrice Mériaudeau, et al.. (2023). A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma. Data. 8(5). 79–79. 28 indexed citations
7.
Lin, Siyu, et al.. (2023). Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets. Journal of Imaging. 9(6). 123–123. 3 indexed citations
8.
Lin, Siyu, et al.. (2023). Biomechanical properties of 3D printable material usable for synthetic personalized healthy human aorta. International Journal of Bioprinting. 9(4). 736–736. 2 indexed citations
9.
Lin, Siyu, Alexandre Cochet, Michel Rochette, et al.. (2023). Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning. Magnetic Resonance Materials in Physics Biology and Medicine. 36(5). 687–700. 5 indexed citations
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Lin, Siyu, Paul M. Walker, Serge Aho, et al.. (2022). Aortic local biomechanical properties in ascending aortic aneurysms. Acta Biomaterialia. 149. 40–50. 11 indexed citations
12.
Dygai‐Cochet, Inna, Benoît Presles, Olivier Chevallier, et al.. (2021). Impact of contouring methods on pre-treatment and post-treatment dosimetry for the prediction of tumor control and survival in HCC patients treated with selective internal radiation therapy. EJNMMI Research. 11(1). 24–24. 7 indexed citations
13.
Modat, Marc, et al.. (2020). Image Registration Using the 'NiftyReg' Library [R package RNiftyReg version 2.7.0]. 1 indexed citations
14.
Presles, Benoît, François Brunotte, Charles Coutant, et al.. (2019). Biological correlates of tumor perfusion and its heterogeneity in newly diagnosed breast cancer using dynamic first-pass 18F-FDG PET/CT. European Journal of Nuclear Medicine and Molecular Imaging. 47(5). 1103–1115. 14 indexed citations
15.
Antonelli, Michela, M. Jorge Cardoso, Edward W. Johnston, et al.. (2018). GAS: A genetic atlas selection strategy in multi-atlas segmentation framework. Medical Image Analysis. 52. 97–108. 16 indexed citations
16.
Burgos, Ninon, Filipa Guerreiro, Jamie R. McClelland, et al.. (2017). Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Physics in Medicine and Biology. 62(11). 4237–4253. 28 indexed citations
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Debayle, Johan & Benoît Presles. (2016). Rigid image registration by General Adaptive Neighborhood matching. Pattern Recognition. 55. 45–57. 18 indexed citations
19.
Presles, Benoît, Marie‐Claude Biston, Hervé Liebgott, et al.. (2014). Semiautomatic registration of 3D transabdominal ultrasound images for patient repositioning during postprostatectomy radiotherapy. Medical Physics. 41(12). 122903–122903. 9 indexed citations
20.
Presles, Benoît, et al.. (2014). Impact of probe pressure variability on prostate localization for ultrasound-based image-guided radiotherapy. Radiotherapy and Oncology. 111(1). 132–137. 24 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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