T. Heimann
Impact in
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Image Retrieval and Classification Techniques
Papers in
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- Medical Image Segmentation Techniques 3
- Surgery 2
- Organ Transplantation Techniques and Outcomes 2
- Co-authors
- Ivo Wolf (3 shared papers)Hans‐Peter Meinzer (2 shared papers)Hans-Peter Meinzer (2 shared papers)Tobias Kunert (1 shared paper)Markus W. Büchler (2 shared papers)Peter Schemmer (2 shared papers)Jan Schmidt (1 shared paper)Boris Radeleff (1 shared paper)
In The Last Decade
T. Heimann
9 papers receiving 133 citations
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 59
- Computer Graphics and Computer-Aided Design 7
- Hepatology 11
- Radiology, Nuclear Medicine and Imaging 25
- Health Informatics 1
Countries citing papers authored by T. Heimann
This map shows the geographic impact of T. Heimann'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 T. Heimann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Heimann more than expected).
Fields of papers citing papers by T. Heimann
This network shows the impact of papers produced by T. Heimann. 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 T. Heimann. The network helps show where T. Heimann may publish in the future.
Co-authors
The 25 scholars most cited alongside T. Heimann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 28 | |
| 2 | 2021 | 23 | |
| 3 | New methods for leak detection and contour correction in seeded region growing segmentation | 2004 | 19 |
| 4 | 2007 | 18 | |
| 5 | 2004 | 17 | |
| 6 | 2012 | 15 | |
| 7 | 2007 | 8 | |
| 8 | 2008 | 6 | |
| 9 | 2023 | 1 |
About T. Heimann
T. Heimann is a scholar working on Computer Vision and Pattern Recognition, Surgery, Pulmonary and Respiratory Medicine, Public Health, Environmental and Occupational Health and Biomedical Engineering, having authored 9 papers that have together received 135 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), Organ Transplantation Techniques and Outcomes (2 papers), Medical Imaging and Analysis (2 papers), Organ Donation and Transplantation (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Pancreatic and Hepatic Oncology Research (1 paper), Renal and Vascular Pathologies (1 paper) and Liver Disease and Transplantation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (59 citations), Computer Graphics and Computer-Aided Design (7 citations), Hepatology (11 citations), Radiology, Nuclear Medicine and Imaging (25 citations) and Health Informatics (1 citation). T. Heimann has collaborated with scholars based in Germany, Algeria and Denmark. Frequent co-authors include Ivo Wolf, Hans‐Peter Meinzer, Hans-Peter Meinzer, Tobias Kunert, Markus W. Büchler, Peter Schemmer, Jan Schmidt, Boris Radeleff, L. Fischer and Jan‐Oliver Neumann. Their work appears in journals such as IEEE Transactions on Medical Imaging, Methods of Information in Medicine, American Journal of Transplantation, Physics in Medicine and Biology and Annals of the Rheumatic Diseases.
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.