Sarah Leclerc

501 total citations
21 papers, 286 citations indexed

About

Sarah Leclerc is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sarah Leclerc has authored 21 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Cardiology and Cardiovascular Medicine and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sarah Leclerc's work include Cardiac Valve Diseases and Treatments (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cardiovascular Function and Risk Factors (6 papers). Sarah Leclerc is often cited by papers focused on Cardiac Valve Diseases and Treatments (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cardiovascular Function and Risk Factors (6 papers). Sarah Leclerc collaborates with scholars based in France, Norway and Canada. Sarah Leclerc's co-authors include Olivier Bernard, Andreas Østvik, Lasse Løvstakken, Erik Smistad, Thomas Grenier, Ivar Mjåland Salte, Alain Lalande, Carole Lartizien, Pierre‐Marc Jodoin and Thuy Mi Nguyen and has published in prestigious journals such as Scientific Reports, Soil Biology and Biochemistry and IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control.

In The Last Decade

Sarah Leclerc

20 papers receiving 282 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarah Leclerc France 10 179 143 66 50 49 21 286
Hany Girgis Netherlands 9 144 0.8× 141 1.0× 29 0.4× 46 0.9× 50 1.0× 32 276
Azira Khalil Malaysia 12 150 0.8× 72 0.5× 58 0.9× 56 1.1× 38 0.8× 24 340
Ivar Mjåland Salte Norway 7 186 1.0× 187 1.3× 17 0.3× 45 0.9× 24 0.5× 14 257
Gregory Holste United States 10 139 0.8× 82 0.6× 21 0.3× 23 0.5× 34 0.7× 21 257
Saman Nouranian Canada 9 94 0.5× 59 0.4× 78 1.2× 72 1.4× 59 1.2× 13 237
Hélène Langet France 10 333 1.9× 385 2.7× 28 0.4× 60 1.2× 40 0.8× 23 511
Matthew Sinclair United Kingdom 12 230 1.3× 173 1.2× 60 0.9× 87 1.7× 16 0.3× 23 395
Mostafa Ghelich Oghli Iran 9 237 1.3× 62 0.4× 97 1.5× 107 2.1× 39 0.8× 19 386
Davis M. Vigneault United States 10 223 1.2× 360 2.5× 42 0.6× 50 1.0× 50 1.0× 21 492
Qianjun Jia China 9 167 0.9× 40 0.3× 21 0.3× 43 0.9× 66 1.3× 28 267

Countries citing papers authored by Sarah Leclerc

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Leclerc

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Leclerc

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Leclerc. A scholar is included among the top collaborators of Sarah Leclerc 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 Sarah Leclerc. Sarah Leclerc 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.
Leclerc, Sarah, et al.. (2024). Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks. Journal of Imaging. 10(8). 203–203.
2.
Leclerc, Sarah, et al.. (2024). Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks. European Archives of Oto-Rhino-Laryngology. 281(6). 2921–2930. 2 indexed citations
3.
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
4.
Leclerc, Sarah, Pierre Barré, Sabine Houot, et al.. (2023). Are carbon-storing soils more sensitive to climate change? A laboratory evaluation for agricultural temperate soils. Soil Biology and Biochemistry. 183. 109043–109043. 12 indexed citations
5.
6.
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.
8.
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
9.
Guigou, C., et al.. (2023). Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges. Journal of Clinical Medicine. 12(16). 5398–5398. 12 indexed citations
10.
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
11.
Lalande, Alain, Sarah Leclerc, Khalid Ambarki, et al.. (2023). 4D segmentation of the thoracic aorta from 4D flow MRI using deep learning. Magnetic Resonance Imaging. 99. 20–25. 6 indexed citations
12.
Leclerc, Sarah, et al.. (2022). Are Carbon-Storing Soils More Sensitive to Climate Change? A Laboratory Evaluation for Agricultural Temperate Soils. SSRN Electronic Journal. 1 indexed citations
13.
Smistad, Erik, Andreas Østvik, Ivar Mjåland Salte, et al.. (2020). Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 67(12). 2595–2604. 53 indexed citations
14.
Leclerc, Sarah, Erik Smistad, Andreas Østvik, et al.. (2020). LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 67(12). 2519–2530. 38 indexed citations
15.
Smistad, Erik, Ivar Mjåland Salte, Andreas Østvik, et al.. (2019). Segmentation of apical long axis, four- and two-chamber views using deep neural networks. HAL (Le Centre pour la Communication Scientifique Directe). 14 indexed citations
16.
Leclerc, Sarah, Pierre‐Marc Jodoin, Lasse Løvstakken, et al.. (2019). RU-Net: A refining segmentation network for 2D echocardiography. HAL (Le Centre pour la Communication Scientifique Directe). 1160–1163. 18 indexed citations
17.
18.
Leclerc, Sarah, Erik Smistad, Thomas Grenier, et al.. (2018). Deep Learning Applied to Multi-Structure Segmentation in 2D Echocardiography: A Preliminary Investigation of the Required Database Size. HAL (Le Centre pour la Communication Scientifique Directe). 1–4. 8 indexed citations
19.
Leclerc, Sarah, et al.. (2017). A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data. 2017 IEEE International Ultrasonics Symposium (IUS). 1–4. 17 indexed citations
20.
Leclerc, Sarah, et al.. (2017). A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data. 2017 IEEE International Ultrasonics Symposium (IUS). 1–1. 22 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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