Thomas M. Deserno
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Biomedical Engineering
- Neurology
- Co-authors
- Torsten KuhlenTil AachEkaterina SirazitdinovaAlexander HorschStephan JonasCord SpreckelsenStefan WolfartKaran Sikka
- Topics
- Medical Image Segmentation Techniques (4 papers)Dental Radiography and Imaging (3 papers)Brain Tumor Detection and Classification (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionOccupational TherapyRadiology, Nuclear Medicine and Imaging
- Partner nations
- GermanyIndiaUnited States
In The Last Decade
Thomas M. Deserno
10 papers receiving 221 citations
Peers
Comparison fields: 5 of 84
- Computer Vision and Pattern Recognition 97
- Radiology, Nuclear Medicine and Imaging 66
- Artificial Intelligence 50
- Biomedical Engineering 30
- Neurology 23
Countries citing papers authored by Thomas M. Deserno
This map shows the geographic impact of Thomas M. Deserno'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 Thomas M. Deserno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas M. Deserno more than expected).
Fields of papers citing papers by Thomas M. Deserno
This network shows the impact of papers produced by Thomas M. Deserno. 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 Thomas M. Deserno. The network helps show where Thomas M. Deserno may publish in the future.
Co-authorship network of co-authors of Thomas M. Deserno
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas M. Deserno. A scholar is included among the top collaborators of Thomas M. Deserno 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 Thomas M. Deserno. Thomas M. Deserno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 10 | |
| 4 | 16 | |
| 5 | 12 | |
| 6 | 8 | |
| 7 | 8 | |
| 8 | 101 | |
| 9 | 57 | |
| 10 | 3 | |
| 11 | 8 | |
| 12 | 3 |
About Thomas M. Deserno
Thomas M. Deserno is a scholar working on Equine, Oral Surgery and Computer Graphics and Computer-Aided Design, having authored 12 papers that have together received 226 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (4 papers), Dental Radiography and Imaging (3 papers) and Brain Tumor Detection and Classification (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (97 citations), Occupational Therapy (12 citations) and Radiology, Nuclear Medicine and Imaging (66 citations). Thomas M. Deserno has collaborated with scholars based in Germany, India and United States. Frequent co-authors include Torsten Kuhlen, Til Aach, Ekaterina Sirazitdinova, Alexander Horsch, Stephan Jonas, Cord Spreckelsen, Stefan Wolfart, Karan Sikka, Christoph Grouls and Taşkın Tuna. Their work appears in journals such as Clinical Oral Implants Research, BMC Veterinary Research and Journal of Digital 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.