David Rousseau

2.9k total citations
123 papers, 1.9k citations indexed

About

David Rousseau is a scholar working on Plant Science, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, David Rousseau has authored 123 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Plant Science, 20 papers in Biomedical Engineering and 19 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in David Rousseau's work include Smart Agriculture and AI (22 papers), Spectroscopy and Chemometric Analyses (15 papers) and Leaf Properties and Growth Measurement (13 papers). David Rousseau is often cited by papers focused on Smart Agriculture and AI (22 papers), Spectroscopy and Chemometric Analyses (15 papers) and Leaf Properties and Growth Measurement (13 papers). David Rousseau collaborates with scholars based in France, Türkiye and United States. David Rousseau's co-authors include Étienne Belin, François Chapeau‐Blondeau, Gamal ElMasry, Pejman Rasti, N. S. Mandour, Helin Dutağacı, Salim S. Al‐Rejaie, Carole Frindel, Valérie Caffier and Gilles Galopin and has published in prestigious journals such as Applied Physics Letters, PLoS ONE and Analytical Chemistry.

In The Last Decade

David Rousseau

110 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Rousseau France 22 892 309 293 262 214 123 1.9k
Sotirios A. Tsaftaris United Kingdom 28 1.0k 1.2× 580 1.9× 154 0.5× 226 0.9× 219 1.0× 138 3.3k
Adel Hafiane France 22 892 1.0× 462 1.5× 232 0.8× 162 0.6× 217 1.0× 70 1.9k
Artzai Picón Spain 21 1.1k 1.3× 265 0.9× 460 1.6× 209 0.8× 56 0.3× 61 2.8k
Shiv Ram Dubey India 31 642 0.7× 221 0.7× 555 1.9× 396 1.5× 94 0.4× 69 4.7k
Davut Hanbay Türkiye 25 538 0.6× 172 0.6× 255 0.9× 127 0.5× 155 0.7× 104 2.1k
Hao Lü China 28 1.1k 1.3× 638 2.1× 380 1.3× 137 0.5× 202 0.9× 101 2.6k
Ulf Geir Indahl Norway 26 150 0.2× 77 0.2× 568 1.9× 214 0.8× 85 0.4× 64 1.6k
Yanchao Zhang China 20 551 0.6× 313 1.0× 187 0.6× 100 0.4× 187 0.9× 69 1.5k
Gladimir V. G. Baranoski Canada 20 180 0.2× 181 0.6× 69 0.2× 112 0.4× 91 0.4× 103 1.1k
Lan Huang China 25 524 0.6× 48 0.2× 132 0.5× 307 1.2× 53 0.2× 132 1.7k

Countries citing papers authored by David Rousseau

Since Specialization
Citations

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

Fields of papers citing papers by David Rousseau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Rousseau

This figure shows the co-authorship network connecting the top 25 collaborators of David Rousseau. A scholar is included among the top collaborators of David Rousseau 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 David Rousseau. David Rousseau 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.
Franconi, Florence, et al.. (2024). Quantitative MRI for brain lesion diagnosis in dogs and cats: A comprehensive overview. Veterinary Radiology & Ultrasound. 65(6). 849–864.
2.
Rousseau, David, et al.. (2024). Online Bayesian Adaptive Sampling for Nonlinear Model: Application to Plant Phenotyping. SPIRE - Sciences Po Institutional REpository. 2537–2541.
3.
Rasti, Pejman, et al.. (2023). Growth Data—An automatic solution for seedling growth analysis via RGB-Depth imaging sensors. SoftwareX. 24. 101572–101572. 1 indexed citations
4.
Rousseau, David, et al.. (2023). Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM). Methods in molecular biology. 2655. 183–200. 2 indexed citations
5.
Boureau, Tristan, et al.. (2022). Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset. Frontiers in Plant Science. 13. 969205–969205. 9 indexed citations
6.
Cochet‐Escartin, Olivier, François Ducray, Mathieu Gabut, et al.. (2022). Machine learning-based detection of label-free cancer stem-like cell fate. Scientific Reports. 12(1). 19066–19066. 5 indexed citations
7.
8.
Leclerc, Pierre, Carole Frindel, Pierre‐François Brevet, et al.. (2020). Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
9.
Desgeorges, Thibaut, Sophie Liot, David Rousseau, et al.. (2019). Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle. Skeletal Muscle. 9(1). 2–2. 53 indexed citations
10.
11.
Rasti, Pejman, et al.. (2019). Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in Biology and Medicine. 116. 103579–103579. 13 indexed citations
12.
Rasti, Pejman, et al.. (2018). Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments. Symmetry. 10(12). 666–666. 6 indexed citations
13.
Rousseau, David, et al.. (2018). Toward quantitative and reproducible clinical use of OCT-Angiography. PLoS ONE. 13(7). e0197588–e0197588. 3 indexed citations
14.
Moussata, Driffa, et al.. (2016). Robust graph representation of images with underlying structural networks. Application to the classification of vascular networks of mice’s colon. Pattern Recognition Letters. 87. 29–37. 2 indexed citations
15.
Frindel, Carole, Marlène Wiart, Max Langer, et al.. (2014). Computer vision tools to optimize reconstruction parameters in x-ray in-line phase tomography. Physics in Medicine and Biology. 59(24). 7767–7775. 5 indexed citations
16.
Beretta, Simone, Davide Carone, Mattéo Riva, et al.. (2014). Cerebral collateral flow defines topography and evolution of molecular penumbra in experimental ischemic stroke. Neurobiology of Disease. 74. 305–313. 19 indexed citations
17.
Rousseau, David, et al.. (2011). Joint acquisition-processing approach to optimize observation scales in noisy imaging. Optics Letters. 36(6). 972–972. 2 indexed citations
18.
Humeau‐Heurtier, Anne, François Chapeau‐Blondeau, David Rousseau, et al.. (2008). Multifractality, sample entropy, and wavelet analyses for age-related changes in the peripheral cardiovascular system: Preliminary results. Medical Physics. 35(2). 717–723. 47 indexed citations
19.
Humeau‐Heurtier, Anne, François Chapeau‐Blondeau, David Rousseau, et al.. (2007). Multifractality in the Peripheral Cardiovascular System from Pointwise Hölder Exponents of Laser Doppler Flowmetry Signals. Biophysical Journal. 93(12). L59–L61. 13 indexed citations
20.
Rousseau, David, et al.. (1977). Performance Prediction Method for a Wing-in-Ground Effect Vehicle with Blowing Under the Wing.. Defense Technical Information Center (DTIC). 2 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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