Daniel B. Russakoff

1.6k total citations
38 papers, 822 citations indexed

About

Daniel B. Russakoff is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daniel B. Russakoff has authored 38 papers receiving a total of 822 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Ophthalmology and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daniel B. Russakoff's work include Retinal Imaging and Analysis (17 papers), Retinal Diseases and Treatments (13 papers) and Medical Image Segmentation Techniques (11 papers). Daniel B. Russakoff is often cited by papers focused on Retinal Imaging and Analysis (17 papers), Retinal Diseases and Treatments (13 papers) and Medical Image Segmentation Techniques (11 papers). Daniel B. Russakoff collaborates with scholars based in United States, United Kingdom and Italy. Daniel B. Russakoff's co-authors include Torsten Rohlfing, Calvin R. Maurer, Jonathan D. Oakley, Kensaku Mori, Adam M. Dubis, Sobha Sivaprasad, John R. Adler, Daniel Rueckert, Joachim Denzler and Martin Herman and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, PLoS ONE and IEEE Transactions on Medical Imaging.

In The Last Decade

Daniel B. Russakoff

36 papers receiving 779 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel B. Russakoff United States 14 452 351 244 163 69 38 822
Yanye Lu China 19 487 1.1× 298 0.8× 139 0.6× 445 2.7× 74 1.1× 71 989
Shuqian Luo China 12 289 0.6× 201 0.6× 109 0.4× 136 0.8× 93 1.3× 42 499
Yicheng Wu China 12 325 0.7× 392 1.1× 130 0.5× 128 0.8× 50 0.7× 32 775
Yufan He United States 16 592 1.3× 445 1.3× 233 1.0× 338 2.1× 19 0.3× 28 1.0k
Denis P. Shamonin Netherlands 10 441 1.0× 207 0.6× 63 0.3× 170 1.0× 85 1.2× 22 842
Julien Jomier United States 13 308 0.7× 259 0.7× 43 0.2× 175 1.1× 53 0.8× 32 746
Ali Gooya United Kingdom 17 540 1.2× 462 1.3× 25 0.1× 228 1.4× 39 0.6× 50 1.1k
P.E. Undrill United Kingdom 14 329 0.7× 247 0.7× 153 0.6× 92 0.6× 15 0.2× 35 697
Gábor Németh Hungary 22 1.1k 2.4× 121 0.3× 847 3.5× 107 0.7× 71 1.0× 85 1.4k
Nishant Ravikumar United Kingdom 15 325 0.7× 205 0.6× 48 0.2× 142 0.9× 13 0.2× 63 669

Countries citing papers authored by Daniel B. Russakoff

Since Specialization
Citations

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

Fields of papers citing papers by Daniel B. Russakoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel B. Russakoff

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel B. Russakoff. A scholar is included among the top collaborators of Daniel B. Russakoff 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 B. Russakoff. Daniel B. Russakoff 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.
Magrath, George N., Daniel B. Russakoff, Jonathan D. Oakley, et al.. (2025). Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study. American Journal of Ophthalmology. 273. 176–181. 3 indexed citations
2.
Sharma, Sarit, Paola Marolo, Daniel B. Russakoff, et al.. (2025). Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy. Eye. 39(3). 570–577. 2 indexed citations
3.
Borrelli, Enrico, Jonathan D. Oakley, Daniel B. Russakoff, et al.. (2023). Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration. Eye. 38(3). 537–544. 8 indexed citations
4.
5.
Pereira, Austin, et al.. (2022). Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume. Ophthalmic surgery, lasers & imaging retina. 53(4). 208–214. 2 indexed citations
6.
Izzi, Jessica, Suzanne E. Queen, Patrick M. Tarwater, et al.. (2021). Combining In Vivo Corneal Confocal Microscopy With Deep Learning–Based Analysis Reveals Sensory Nerve Fiber Loss in Acute Simian Immunodeficiency Virus Infection. Cornea. 40(5). 635–642. 7 indexed citations
7.
Oakley, Jonathan D., et al.. (2021). Automated Deep Learning-Based Multi-Class Fluid Segmentation inSwept-Source Optical Coherence Tomography Images. 12(8). 24–37. 1 indexed citations
8.
9.
Oakley, Jonathan D., Daniel B. Russakoff, Jessica Izzi, et al.. (2020). Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images. Eye and Vision. 7(1). 27–27. 18 indexed citations
10.
Oakley, Jonathan D., et al.. (2018). Automated Analysis of In Vivo Confocal Microscopy Corneal Images Using Deep Learning. Investigative Ophthalmology & Visual Science. 59(9). 1799–1799. 3 indexed citations
11.
Oakley, Jonathan D., et al.. (2018). Changes in volume of various retinal layers over time in early and intermediate age-related macular degeneration. Eye. 33(3). 428–434. 45 indexed citations
12.
Oakley, Jonathan D., Magí Andorrà, Elena H. Martínez‐Lapiscina, Daniel B. Russakoff, & Pablo Villoslada. (2016). Comparison of Automated Retinal Segmentation across OCT Devices using Independent Analysis Software. Investigative Ophthalmology & Visual Science. 57(12). 5956–5956. 2 indexed citations
13.
Keller, Johannes, Jonathan D. Oakley, Daniel B. Russakoff, et al.. (2015). Changes in macular layers in the early course of non-arteritic ischaemic optic neuropathy. Graefe s Archive for Clinical and Experimental Ophthalmology. 254(3). 561–567. 20 indexed citations
14.
Lujan, Brandon J., Daniel B. Russakoff, Jonathan D. Oakley, Mona K. Garvin, & Austin Roorda. (2014). True Outer Nuclear Layer Volumes Using Directional Optical Coherence Tomography. Investigative Ophthalmology & Visual Science. 55(13). 4803–4803.
15.
Oakley, Jonathan D., et al.. (2014). Assessing Manual versus Automated Segmentation of the Macula using Optical Coherence Tomography. Investigative Ophthalmology & Visual Science. 55(13). 4790–4790. 6 indexed citations
16.
Moussavi, Farshid, Yu Wang, Peter Lorenzen, et al.. (2014). A Unified Graphical Models Framework for Automated Mitosis Detection in Human Embryos. IEEE Transactions on Medical Imaging. 33(7). 1551–1562. 9 indexed citations
17.
Johung, Tessa, et al.. (2013). The Prevalence of Cirrus SD-OCT Ganglion Cell Segmentation Errors in High Myopes. Investigative Ophthalmology & Visual Science. 54(15). 4845–4845. 1 indexed citations
18.
Rohlfing, Torsten, Daniel B. Russakoff, Joachim Denzler, Kensaku Mori, & Calvin R. Maurer. (2005). Progressive attenuation fields: Fast 2D‐3D image registration without precomputation. Medical Physics. 32(9). 2870–2880. 41 indexed citations
19.
Rohlfing, Torsten, et al.. (2004). Markerless Real-Time Target Region Tracking: Application to Frameless Sterotactic Radiosurgery.. Vision Modeling and Visualization. 5–12. 1 indexed citations
20.
Rohlfing, Torsten, Daniel B. Russakoff, & Calvin R. Maurer. (2004). Performance-Based Classifier Combination in Atlas-Based Image Segmentation Using Expectation-Maximization Parameter Estimation. IEEE Transactions on Medical Imaging. 23(8). 983–994. 204 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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