Seth Billings

558 total citations
19 papers, 370 citations indexed

About

Seth Billings is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Seth Billings has authored 19 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Aerospace Engineering and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Seth Billings's work include Robotics and Sensor-Based Localization (6 papers), Medical Image Segmentation Techniques (4 papers) and Ultrasound Imaging and Elastography (3 papers). Seth Billings is often cited by papers focused on Robotics and Sensor-Based Localization (6 papers), Medical Image Segmentation Techniques (4 papers) and Ultrasound Imaging and Elastography (3 papers). Seth Billings collaborates with scholars based in United States, Israel and Germany. Seth Billings's co-authors include Russell H. Taylor, Philippe Burlina, Neil Joshi, Jemima Albayda, Emad M. Boctor, Russell G. Taylor, Elise Ng, John N. Aucott, Alison W. Rebman and Hyun‐Jae Kang and has published in prestigious journals such as PLoS ONE, Medical Image Analysis and Computers in Biology and Medicine.

In The Last Decade

Seth Billings

17 papers receiving 363 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seth Billings United States 9 121 105 79 62 55 19 370
Fernando Arámbula Cosı́o Mexico 12 246 2.0× 47 0.4× 210 2.7× 20 0.3× 90 1.6× 61 688
Tianyu Fu China 10 97 0.8× 21 0.2× 79 1.0× 18 0.3× 59 1.1× 61 293
Christian Daul France 12 201 1.7× 59 0.6× 75 0.9× 19 0.3× 105 1.9× 62 461
Ayushi Sinha United States 9 158 1.3× 104 1.0× 40 0.5× 26 0.4× 25 0.5× 23 312
Monan Wang China 11 103 0.9× 18 0.2× 113 1.4× 28 0.5× 28 0.5× 55 380
Stanley Dunn United States 16 152 1.3× 22 0.2× 86 1.1× 37 0.6× 94 1.7× 46 615
William D. Richard United States 10 230 1.9× 27 0.3× 199 2.5× 31 0.5× 207 3.8× 30 523
Grzegorz Soza Germany 12 197 1.6× 18 0.2× 148 1.9× 55 0.9× 227 4.1× 26 544
Erwan Kerrien France 11 134 1.1× 25 0.2× 95 1.2× 30 0.5× 79 1.4× 32 471
Atsushi Nakazawa Japan 12 234 1.9× 105 1.0× 54 0.7× 34 0.5× 9 0.2× 33 460

Countries citing papers authored by Seth Billings

Since Specialization
Citations

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

Fields of papers citing papers by Seth Billings

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seth Billings

This figure shows the co-authorship network connecting the top 25 collaborators of Seth Billings. A scholar is included among the top collaborators of Seth Billings 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 Seth Billings. Seth Billings is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
2.
Christie, Breanne, Avi Caspi, Francesco V. Tenore, et al.. (2023). Perception of Dynamic Displays by People with Argus® II Retinal Prostheses. 1–4.
3.
Christie, Breanne, Avi Caspi, Francesco V. Tenore, et al.. (2022). Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only. Journal of Neural Engineering. 19(3). 36033–36033. 6 indexed citations
4.
Sinha, Ayushi, Seth Billings, Austin Reiter, et al.. (2019). The deformable most-likely-point paradigm. Medical Image Analysis. 55. 148–164. 16 indexed citations
5.
Burlina, Philippe, et al.. (2019). Unsupervised Deep Novelty Detection: Application To Muscle Ultrasound And Myositis Screening. 12. 1910–1914. 4 indexed citations
6.
Burlina, Philippe, Neil Joshi, Elise Ng, et al.. (2018). Automated detection of erythema migrans and other confounding skin lesions via deep learning. Computers in Biology and Medicine. 105. 151–156. 43 indexed citations
7.
Burlina, Philippe, Neil Joshi, Seth Billings, I-Jeng Wang, & Jemima Albayda. (2018). Deep embeddings for novelty detection in myopathy. Computers in Biology and Medicine. 105. 46–53. 18 indexed citations
8.
Burlina, Philippe, Seth Billings, Neil Joshi, & Jemima Albayda. (2017). Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods. PLoS ONE. 12(8). e0184059–e0184059. 107 indexed citations
9.
Joshi, Neil, et al.. (2017). Machine Learning Methods for 1D Ultrasound Breast Cancer Screening. 33. 711–715. 2 indexed citations
10.
Billings, Seth, Ayushi Sinha, Austin Reiter, et al.. (2016). Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm. Lecture notes in computer science. 9902. 133–141. 8 indexed citations
11.
Wolfe, Kevin, Dean M. Kleissas, Seth Billings, et al.. (2016). Embedded clutter reduction and face detection algorithms for a visual prosthesis. PubMed. 2016. 411–414. 4 indexed citations
12.
Billings, Seth, Jemima Albayda, & Philippe Burlina. (2016). Ultrasound image analysis for myopathy detection. 1461–1465. 3 indexed citations
13.
Billings, Seth & Russell H. Taylor. (2015). Generalized iterative most likely oriented-point (G-IMLOP) registration. International Journal of Computer Assisted Radiology and Surgery. 10(8). 1213–1226. 29 indexed citations
14.
Billings, Seth, Emad M. Boctor, & Russell H. Taylor. (2015). Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment. PLoS ONE. 10(3). e0117688–e0117688. 64 indexed citations
15.
Billings, Seth, Hyun‐Jae Kang, Lei Chen, et al.. (2015). Minimally invasive registration for computer-assisted orthopedic surgery: combining tracked ultrasound and bone surface points via the P-IMLOP algorithm. International Journal of Computer Assisted Radiology and Surgery. 10(6). 761–771. 3 indexed citations
16.
Kang, Hyun‐Jae, et al.. (2014). Elastography Using Multi-Stream GPU: An Application to Online Tracked Ultrasound Elastography, In-Vivo and the da Vinci Surgical System. PLoS ONE. 9(12). e115881–e115881. 7 indexed citations
17.
Billings, Seth & Russell G. Taylor. (2014). Iterative Most Likely Oriented Point Registration. Lecture notes in computer science. 17(Pt 1). 178–185. 31 indexed citations
18.
Billings, Seth, et al.. (2012). System for robot-assisted real-time laparoscopic ultrasound elastography. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8316. 83161W–83161W. 22 indexed citations
19.
Billings, Seth, Ankur Kapoor, Matthias S. Keil, Bradford J. Wood, & Emad M. Boctor. (2011). A hybrid surface/image-based approach to facilitate ultrasound/CT registration. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7968. 79680V–79680V. 3 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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