Daniel Sage

9.1k total citations · 5 hit papers
71 papers, 6.5k citations indexed

About

Daniel Sage is a scholar working on Biophysics, Molecular Biology and Media Technology. According to data from OpenAlex, Daniel Sage has authored 71 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Biophysics, 17 papers in Molecular Biology and 13 papers in Media Technology. Recurrent topics in Daniel Sage's work include Advanced Fluorescence Microscopy Techniques (22 papers), Cell Image Analysis Techniques (18 papers) and Image Processing Techniques and Applications (8 papers). Daniel Sage is often cited by papers focused on Advanced Fluorescence Microscopy Techniques (22 papers), Cell Image Analysis Techniques (18 papers) and Image Processing Techniques and Applications (8 papers). Daniel Sage collaborates with scholars based in Switzerland, France and United Kingdom. Daniel Sage's co-authors include Michaël Unser, Aurélien F. Stalder, P. Hoffmann, G. Kulik, Luca Barbieri, Nikolaos Stergiopulos, Dimitri Van De Ville, Michael Müller, Thierry Blu and Tobias Melchior and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Genes & Development and The EMBO Journal.

In The Last Decade

Daniel Sage

70 papers receiving 6.4k citations

Hit Papers

Experimental investigation of collagen waviness and orien... 2006 2026 2012 2019 2011 2006 2010 2017 2016 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Sage Switzerland 32 1.5k 1.4k 1.2k 650 635 71 6.5k
James N. Turner United States 43 2.7k 1.8× 803 0.6× 779 0.6× 383 0.6× 469 0.7× 168 6.7k
Alberto Diaspro Italy 55 4.6k 3.0× 3.8k 2.8× 4.2k 3.4× 197 0.3× 859 1.4× 506 12.8k
Subra Suresh United States 43 3.7k 2.4× 900 0.7× 413 0.3× 258 0.4× 401 0.6× 61 9.7k
Tony Yu United States 28 1.2k 0.8× 808 0.6× 372 0.3× 532 0.8× 216 0.3× 95 7.2k
James B. Pawley United States 15 1.4k 0.9× 1.2k 0.9× 2.0k 1.7× 210 0.3× 227 0.4× 27 4.4k
Yu Sun Canada 69 8.6k 5.7× 2.2k 1.6× 626 0.5× 714 1.1× 2.1k 3.3× 596 17.7k
Qingming Luo China 62 4.6k 3.0× 3.5k 2.5× 2.9k 2.4× 308 0.5× 389 0.6× 560 14.6k
Lianqing Liu China 44 4.7k 3.1× 666 0.5× 335 0.3× 161 0.2× 701 1.1× 506 8.1k
Manuel Guizar‐Sicairos Switzerland 50 1.7k 1.1× 383 0.3× 276 0.2× 644 1.0× 100 0.2× 178 8.9k
Mark Hiner United States 8 731 0.5× 2.1k 1.5× 511 0.4× 141 0.2× 527 0.8× 17 6.5k

Countries citing papers authored by Daniel Sage

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Sage

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Sage

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Sage. A scholar is included among the top collaborators of Daniel Sage 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 Daniel Sage. Daniel Sage 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.
Soubies, Emmanuel, et al.. (2024). Surpassing light inhomogeneities in structured‐illumination microscopy with FlexSIM. Journal of Microscopy. 296(1). 94–106. 2 indexed citations
2.
Bauer, Sebastian, et al.. (2024). Machine learning in microscopy – insights, opportunities and challenges. Journal of Cell Science. 137(20). 7 indexed citations
3.
Sage, Daniel, et al.. (2023). Quantitative morphological analysis of the T-tubular network of ventricular cardiomyocytes using novel image processing tools. MDC Repository (Max-Delbrueck-Center for Molecular Medicine). 121–121.
4.
Martino, Fabio De, Martin Weigert, Olivier Burri, et al.. (2021). Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features. Journal of Mammary Gland Biology and Neoplasia. 26(2). 101–112. 3 indexed citations
5.
Gómez‐de‐Mariscal, Estibaliz, Wei Ouyang, Laurène Donati, et al.. (2021). DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods. 18(10). 1192–1195. 138 indexed citations
6.
Sage, Daniel, Thanh-an Pham, Hazen P. Babcock, et al.. (2019). Publisher Correction: Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nature Methods. 16(6). 561–561. 2 indexed citations
7.
Sage, Daniel, Thanh-an Pham, Hazen P. Babcock, et al.. (2019). Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nature Methods. 16(5). 387–395. 210 indexed citations
8.
Deschout, Hendrik, et al.. (2017). Investigating Focal Adhesion Substructures by Localization Microscopy. Biophysical Journal. 113(11). 2508–2518. 18 indexed citations
10.
Sage, Daniel, Hagai Kirshner, Thomas Pengo, et al.. (2015). Quantitative evaluation of software packages for single-molecule localization microscopy. Nature Methods. 12(8). 717–724. 254 indexed citations
11.
Bostan, Emrah, et al.. (2014). Phase retrieval by using transport-of-intensity equation and differential interference contrast microscopy. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3939–3943. 8 indexed citations
12.
Unser, Michaël, Daniel Sage, & Ricard Delgado-Gonzalo. (2013). Advanced image processing for biology, and the Open Bio Image Alliance (OBIA). Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–5. 3 indexed citations
13.
Sage, Daniel, Hagai Kirshner, Cédric Vonesch, Stamatios Lefkimmiatis, & Michaël Unser. (2013). Benchmarking image-processing algorithms for biomicroscopy: Reference datasets and perspectives. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–4. 4 indexed citations
14.
Schmitter, Daniel, Daniel Sage, Anastasia Chasapi, et al.. (2013). A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis. Cell Division. 8(1). 6–6. 12 indexed citations
15.
Thévenaz, P., Daniel Sage, & Michaël Unser. (2012). Bi-Exponential Edge-Preserving Smoother. IEEE Transactions on Image Processing. 21(9). 3924–3936. 33 indexed citations
16.
Sage, Daniel, et al.. (2011). Flow measurements in sewers based on image analysis: automatic flow velocity algorithm. Water Science & Technology. 64(5). 1108–1114. 20 indexed citations
17.
Wüstner, Daniel, Jonathan R. Brewer, Luís A. Bagatolli, & Daniel Sage. (2010). Potential of ultraviolet wide‐field imaging and multiphoton microscopy for analysis of dehydroergosterol in cellular membranes. Microscopy Research and Technique. 74(1). 92–108. 25 indexed citations
18.
Wüstner, Daniel, et al.. (2010). Selective Visualization of Fluorescent Sterols in Caenorhabditis elegans by Bleach-Rate-Based Image Segmentation. Traffic. 11(4). 440–454. 38 indexed citations
19.
Ville, Dimitri Van De, et al.. (2008). The Marr wavelet pyramid and multiscale directional image analysis. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–5. 7 indexed citations
20.
Schober, Heiko, Véronique Kalck, Miguel A. Vega-Palas, et al.. (2007). Controlled exchange of chromosomal arms reveals principles driving telomere interactions in yeast. Genome Research. 18(2). 261–271. 64 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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