Laurent Massoptier

752 total citations
23 papers, 543 citations indexed

About

Laurent Massoptier is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Neurology. According to data from OpenAlex, Laurent Massoptier has authored 23 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 10 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Neurology. Recurrent topics in Laurent Massoptier's work include Medical Image Segmentation Techniques (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Brain Tumor Detection and Classification (6 papers). Laurent Massoptier is often cited by papers focused on Medical Image Segmentation Techniques (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Brain Tumor Detection and Classification (6 papers). Laurent Massoptier collaborates with scholars based in Italy, France and Germany. Laurent Massoptier's co-authors include Sergio Casciaro, José Dolz, Maximilien Vermandel, Roberto Franchini, Francesco Conversano, Antonio Malvası, Nicolas Reyns, A. Lay-Ekuakille, Ursula Nestle and Alfonso Maffezzoli and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Medical Physics and Radiotherapy and Oncology.

In The Last Decade

Laurent Massoptier

22 papers receiving 523 citations

Peers

Laurent Massoptier

CVPR

RNMI

NEURO

Hans Meine Germany

Jiawei Sun China

Peijun Hu China

Thomas Langerak Netherlands

Adam P. Harrison United States

Fa Wu China

Jianghong Xiao China

Yuanzhi Cheng China

Rudi Deklerck Belgium

Hans Meine Germany

Laurent Massoptier

164 ×0.6

CVPR

284 ×1.1

RNMI

133 ×0.9

133 ×1.0

58 ×0.8

NEURO

Citations per year, relative to Laurent Massoptier Laurent Massoptier (= 1×) peers Hans Meine

Countries citing papers authored by Laurent Massoptier

Since Specialization

Citations

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

Fields of papers citing papers by Laurent Massoptier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laurent Massoptier

This figure shows the co-authorship network connecting the top 25 collaborators of Laurent Massoptier. A scholar is included among the top collaborators of Laurent Massoptier 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 Laurent Massoptier. Laurent Massoptier 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

Dolz, José, Hortense A. Kirişli, Tobias Fechter, et al.. (2016). Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients. Medical Physics. 43(5). 2569–2580. 19 indexed citations

Dolz, José, S. Ken, Lotfi Chaâri, et al.. (2016). PV-0475: Probability map prediction of relapse areas in glioblastoma patients using multi-parametric MR. Radiotherapy and Oncology. 119. S226–S227. 1 indexed citations

Dolz, José, et al.. (2016). Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study. Computerized Medical Imaging and Graphics. 52. 8–18. 35 indexed citations

Dolz, José, Hortense A. Kirişli, Sonja Adebahr, et al.. (2015). User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy. Journal of Digital Imaging. 29(2). 264–277. 26 indexed citations

Schaefer, Andrea, Maximilien Vermandel, C. Baillet, et al.. (2015). Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation. European Journal of Nuclear Medicine and Molecular Imaging. 43(5). 911–924. 30 indexed citations

Dolz, José, Laurent Massoptier, & Maximilien Vermandel. (2015). Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey. IRBM. 36(4). 200–212. 37 indexed citations

Dolz, José, Anne Laprie, S. Ken, et al.. (2015). Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context. International Journal of Computer Assisted Radiology and Surgery. 11(1). 43–51. 22 indexed citations

Dolz, José, Henri‐Arthur Leroy, Nicolas Reyns, Laurent Massoptier, & Maximilien Vermandel. (2015). A fast and fully automated approach to segment optic nerves on MRI and its application to radiosurgery. 1102–1105. 10 indexed citations

Dolz, José, Laurent Massoptier, & Maximilien Vermandel. (2014). Segmentation algorithms of subcortical brain structures on MRI:a review. LillOA (Université de Lille (University Of Lille)). 1 indexed citations

10.

Dolz, José, Hortense A. Kirişli, R. Viard, & Laurent Massoptier. (2014). Combining watershed and graph cuts methods to segment organs at risk in radiotherapy. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9034. 90343Z–90343Z. 3 indexed citations

11.

Fechter, Tobias, José Dolz, Hortense A. Kirişli, et al.. (2014). Evaluation of an Integrated Minimally Interactive Tool for the Segmentation of Relevant Oar in Lung Cancer. International Journal of Radiation Oncology*Biology*Physics. 90(1). S817–S817.

12.

Conversano, Francesco, Roberto Franchini, Christian Demitri, et al.. (2011). Hepatic Vessel Segmentation for 3D Planning of Liver Surgery. Academic Radiology. 18(4). 461–470. 48 indexed citations

13.

Casciaro, Sergio, Roberto Franchini, Laurent Massoptier, et al.. (2011). Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods. IEEE Sensors Journal. 12(3). 464–473. 42 indexed citations

14.

Massoptier, Laurent, et al.. (2011). COMBINING GRAPH-CUT TECHNIQUE AND ANATOMICAL KNOWLEDGE FOR AUTOMATIC SEGMENTATION OF LUNGS AFFECTED BY DIFFUSE PARENCHYMAL DISEASE IN HRCT IMAGES. International Journal of Image and Graphics. 11(4). 509–529. 2 indexed citations

15.

Greco, Adelaide, Francesco Conversano, Roberto Franchini, et al.. (2011). Improving automatic segmentation of tissue-targeted nanoparticles on echographic images. 28. 488–491. 10 indexed citations

16.

Lamata, Pablo, Piotr Makowski, Laurent Massoptier, et al.. (2010). Use of the Resection Map system as guidance during hepatectomy. Surgical Endoscopy. 24(9). 2327–2337. 48 indexed citations

17.

Massoptier, Laurent, et al.. (2009). Automatic lung segmentation in HRCT images with diffuse parenchymal lung disease using graph-cut. 266–270. 5 indexed citations

18.

Massoptier, Laurent & Sergio Casciaro. (2008). A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. European Radiology. 18(8). 1658–1665. 128 indexed citations

19.

Casciaro, Sergio & Laurent Massoptier. (2007). Automatic Vertebral Morphometry Assessment. Conference proceedings. 2007. 5571–4. 8 indexed citations

20.

Massoptier, Laurent & Sergio Casciaro. (2007). Fully Automatic Liver Segmentation through Graph-Cut Technique. Conference proceedings. 2007. 5243–5246. 58 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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