Imanol Luengo

1.2k total citations
26 papers, 525 citations indexed

About

Imanol Luengo is a scholar working on Surgery, Computer Vision and Pattern Recognition and Oncology. According to data from OpenAlex, Imanol Luengo has authored 26 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Surgery, 9 papers in Computer Vision and Pattern Recognition and 6 papers in Oncology. Recurrent topics in Imanol Luengo's work include Surgical Simulation and Training (11 papers), Medical Image Segmentation Techniques (6 papers) and Colorectal Cancer Screening and Detection (6 papers). Imanol Luengo is often cited by papers focused on Surgical Simulation and Training (11 papers), Medical Image Segmentation Techniques (6 papers) and Colorectal Cancer Screening and Detection (6 papers). Imanol Luengo collaborates with scholars based in United Kingdom, Ireland and United States. Imanol Luengo's co-authors include Danail Stoyanov, Abdolrahim Kadkhodamohammadi, Andrew P. French, G. Polder, Danijela Vukadinovic, Xiaoming Liu, Sotirios A. Tsaftaris, Massimo Minervini, Xi Yin and Jean-Michel Pape and has published in prestigious journals such as SHILAP Revista de lepidopterología, British Journal of Ophthalmology and Medical Image Analysis.

In The Last Decade

Imanol Luengo

24 papers receiving 512 citations

Peers

Imanol Luengo

SURGE

ECOLO

CVPR

Mattia Savardi Italy

Xiangyue Zhang China

Adriane Parraga Brazil

Alexander Braun Germany

Xiangrui Zeng United States

Russell Watkins United Kingdom

Chaijie Duan China

Weihao Xie China

Peijie Huang China

Yuewu Liu China

Mattia Savardi Italy

Imanol Luengo

36 ×0.2

15 ×0.1

SURGE

54 ×0.6

ECOLO

114 ×1.3

CVPR

67 ×0.9

Citations per year, relative to Imanol Luengo Imanol Luengo (= 1×) peers Mattia Savardi

Countries citing papers authored by Imanol Luengo

Since Specialization

Citations

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

Fields of papers citing papers by Imanol Luengo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Imanol Luengo

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

Shah, Neil P., James Wawrzynski, Imanol Luengo, et al.. (2025). Application of real-time artificial intelligence to cataract surgery. British Journal of Ophthalmology. 109(12). 1338–1344.

Sánchez-Matilla, Ricardo, et al.. (2024). DIPO: Differentiable Parallel Operation Blocks for Surgical Neural Architecture Search. IEEE Journal of Biomedical and Health Informatics. 28(9). 5540–5550.

Khan, Danyal Z., John Hanrahan, Simon C. Williams, et al.. (2024). Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery. npj Digital Medicine. 7(1). 314–314. 7 indexed citations

Mehta, Pritesh, et al.. (2024). Hierarchical segmentation of surgical scenes in laparoscopy. International Journal of Computer Assisted Radiology and Surgery. 19(7). 1449–1457. 1 indexed citations

Luengo, Imanol, et al.. (2024). Guided image generation for improved surgical image segmentation. Medical Image Analysis. 97. 103263–103263. 4 indexed citations

Zhang, Jinglu, et al.. (2023). Self-knowledge distillation for surgical phase recognition. International Journal of Computer Assisted Radiology and Surgery. 19(1). 61–68. 4 indexed citations

Sánchez-Matilla, Ricardo, Felix Bragman, David Owen, et al.. (2023). A spatio-temporal network for video semantic segmentation in surgical videos. International Journal of Computer Assisted Radiology and Surgery. 19(2). 375–382. 10 indexed citations

Owen, David, Maria G. Grammatikopoulou, Imanol Luengo, & Danail Stoyanov. (2022). Automated identification of critical structures in laparoscopic cholecystectomy. International Journal of Computer Assisted Radiology and Surgery. 17(12). 2173–2181. 7 indexed citations

Sánchez-Matilla, Ricardo, et al.. (2022). Data-centric multi-task surgical phase estimation with sparse scene segmentation. International Journal of Computer Assisted Radiology and Surgery. 17(5). 953–960. 11 indexed citations

10.

King, Oliver N. F., et al.. (2022). SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales. Frontiers in Cell and Developmental Biology. 10. 842342–842342. 10 indexed citations

11.

Kadkhodamohammadi, Abdolrahim, Imanol Luengo, & Danail Stoyanov. (2022). PATG: position-aware temporal graph networks for surgical phase recognition on laparoscopic videos. International Journal of Computer Assisted Radiology and Surgery. 17(5). 849–856. 20 indexed citations

12.

Flouty, Evangello, Abdolrahim Kadkhodamohammadi, Gwenolé Quellec, et al.. (2021). CaDIS: Cataract dataset for surgical RGB-image segmentation. Medical Image Analysis. 71. 102053–102053. 44 indexed citations

13.

Speidel, Stefanie, Lena Maier‐Hein, Danail Stoyanov, et al.. (2020). Endoscopic Vision Challenge. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations

14.

Kadkhodamohammadi, Abdolrahim, Πέτρος Γιαταγάνας, Gauthier Gras, et al.. (2020). Towards video-based surgical workflow understanding in open orthopaedic surgery. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 9(3). 286–293. 17 indexed citations

15.

Fuentes-Hurtado, Félix, et al.. (2019). EasyLabels: weak labels for scene segmentation in laparoscopic videos. International Journal of Computer Assisted Radiology and Surgery. 14(7). 1247–1257. 20 indexed citations

16.

Luengo, Imanol, Omer F. Ahmad, Matthew Banks, et al.. (2019). Artificial intelligence for the real‐time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof‐of‐concept study. United European Gastroenterology Journal. 7(2). 297–306. 68 indexed citations

17.

Darrow, Michele C., Imanol Luengo, Mark Basham, et al.. (2017). Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench. Journal of Visualized Experiments. 6 indexed citations

18.

Luengo, Imanol, Michele C. Darrow, Matthew C. Spink, et al.. (2017). SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology. 198(1). 43–53. 62 indexed citations

19.

Luengo, Imanol, Mark Basham, & Andrew P. French. (2016). SMURFS: Superpixels from Multi-scale Refinement of Super-regions. Repository@Nottingham (University of Nottingham). 4.1–4.12. 2 indexed citations

20.

Scharr, Hanno, Massimo Minervini, Andrew P. French, et al.. (2015). Leaf segmentation in plant phenotyping: a collation study. Machine Vision and Applications. 27(4). 585–606. 195 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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