Daniel B. Russakoff

36 papers receiving 779 citations

Peers

Daniel B. Russakoff
Comparison fields: 5 of 83
  • Radiology, Nuclear Medicine and Imaging 452
  • Computer Vision and Pattern Recognition 351
  • Ophthalmology 244
  • Biomedical Engineering 163
  • Radiation 69
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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
#WorkIndexed citations
1 3
2 2
3 3
4 8
5 10
6 7
7
Automated Deep Learning-Based Multi-Class Fluid Segmentation inSwept-Source Optical Coherence Tomography Images
1
8 0
9
Automated Analysis of In Vivo Confocal Microscopy Corneal Images Using Deep Learning
3
10 45
11
Comparison of Automated Retinal Segmentation across OCT Devices using Independent Analysis Software
2
12 20
13
Assessing Manual versus Automated Segmentation of the Macula using Optical Coherence Tomography
6
14
True Outer Nuclear Layer Volumes Using Directional Optical Coherence Tomography
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15
The Prevalence of Cirrus SD-OCT Ganglion Cell Segmentation Errors in High Myopes
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16 41
17 110
18
Markerless Real-Time Target Region Tracking: Application to Frameless Sterotactic Radiosurgery.
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19 204
20 13

About Daniel B. Russakoff

Daniel B. Russakoff is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 822 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (17 papers), Retinal Diseases and Treatments (13 papers) and Medical Image Segmentation Techniques (11 papers). The work is most often cited by research in Ophthalmology (244 citations), Radiology, Nuclear Medicine and Imaging (452 citations) and Computer Vision and Pattern Recognition (351 citations). Daniel B. Russakoff has collaborated with scholars based in United States, United Kingdom and Italy. Frequent 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. Their work appears in journals such as Journal of Geophysical Research Atmospheres, PLoS ONE and IEEE Transactions on Medical Imaging.

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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