Heinrich Schulz

554 total citations
20 papers, 326 citations indexed

About

Heinrich Schulz is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Heinrich Schulz has authored 20 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Computer Vision and Pattern Recognition and 7 papers in Biomedical Engineering. Recurrent topics in Heinrich Schulz's work include Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Imaging Techniques and Applications (6 papers). Heinrich Schulz is often cited by papers focused on Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Imaging Techniques and Applications (6 papers). Heinrich Schulz collaborates with scholars based in Germany, Netherlands and United States. Heinrich Schulz's co-authors include Jochen Krücker, Sheng Xu, Bradford J. Wood, Jörn Borgert, Neil Glossop, Anand Viswanathan, Dmitry V. Dylov, Bart Bakker, Irina Fedulova and Steffen Renisch and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Heinrich Schulz

18 papers receiving 312 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Heinrich Schulz Germany 7 134 103 77 76 57 20 326
Brent J. Liu United States 10 190 1.4× 85 0.8× 97 1.3× 71 0.9× 43 0.8× 74 550
Stefan Schmidt Germany 8 181 1.4× 114 1.1× 102 1.3× 41 0.5× 84 1.5× 22 472
Laurent Massoptier Italy 11 252 1.9× 128 1.2× 263 3.4× 142 1.9× 54 0.9× 23 543
Miaofei Han China 11 347 2.6× 93 0.9× 81 1.1× 134 1.8× 19 0.3× 16 481
Baochun He China 11 193 1.4× 103 1.0× 128 1.7× 47 0.6× 72 1.3× 24 352
Oliver Kutter Germany 13 174 1.3× 141 1.4× 227 2.9× 42 0.6× 107 1.9× 25 457
Grzegorz Chlebus Germany 7 216 1.6× 61 0.6× 111 1.4× 120 1.6× 12 0.2× 9 321
Jeroen Bertels Belgium 10 210 1.6× 114 1.1× 127 1.6× 116 1.5× 29 0.5× 15 527
Ian Chan Canada 11 362 2.7× 128 1.2× 156 2.0× 120 1.6× 48 0.8× 32 636
Ruida Cheng United States 9 106 0.8× 143 1.4× 94 1.2× 44 0.6× 36 0.6× 14 309

Countries citing papers authored by Heinrich Schulz

Since Specialization
Citations

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

Fields of papers citing papers by Heinrich Schulz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heinrich Schulz

This figure shows the co-authorship network connecting the top 25 collaborators of Heinrich Schulz. A scholar is included among the top collaborators of Heinrich Schulz 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 Heinrich Schulz. Heinrich Schulz 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.
Sirazitdinov, Ilyas, Heinrich Schulz, Axel Saalbach, Steffen Renisch, & Dmitry V. Dylov. (2022). Tubular shape aware data generation for segmentation in medical imaging. International Journal of Computer Assisted Radiology and Surgery. 17(6). 1091–1099. 3 indexed citations
2.
3.
Bakker, Bart, et al.. (2021). Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders. IEEE Access. 9. 118571–118583. 80 indexed citations
4.
Schulz, Heinrich, et al.. (2019). Pseudo-CT image generation from mDixon MRI images using fully convolutional neural networks. 33–33. 5 indexed citations
5.
Klahr, Paul, Siamak P. Nejad‐Davarani, Heinrich Schulz, et al.. (2019). Impact of CT reconstruction algorithm on auto‐segmentation performance. Journal of Applied Clinical Medical Physics. 20(9). 95–103. 6 indexed citations
6.
Maiwald, Christian, et al.. (2019). Validation of an assistance system for motion analysis. SHILAP Revista de lepidopterología. 2019(3). 75–82.
7.
Heinrich‬, Mattias P., et al.. (2018). Nearest neighbor 3D segmentation with context features. 21–21. 1 indexed citations
8.
Groza, Vladimir, Tom Brosch, Dennis Eschweiler, et al.. (2018). Comparison of deep learning-based techniques for organ segmentation in abdominal CT images. 6 indexed citations
9.
Franz, Astrid, et al.. (2016). Precise anatomy localization in CT data by an improved probabilistic tissue type atlas. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9784. 978444–978444.
10.
Franz, Astrid, et al.. (2015). Annotation-free probabilistic atlas learning for robust anatomy detection in CT images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9413. 941338–941338. 1 indexed citations
11.
Buerger, Christian, Julien Sénégas, Sven Kabus, et al.. (2014). Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging. Medical Physics. 42(1). 69–80. 7 indexed citations
12.
13.
Vik, T., et al.. (2012). PO-0854 SIMULTANEOUS FULLY AUTOMATIC SEGMENTATION OF MALE PELVIC RISK STRUCTURES. Radiotherapy and Oncology. 103. S333–S334. 1 indexed citations
14.
Vik, T., et al.. (2012). A new method for robust organ positioning in CT images. 6533. 338–341. 4 indexed citations
15.
Vik, T., et al.. (2009). Local motion analysis in 4D lung CT using fast groupwise registration. 1749–1752. 1 indexed citations
16.
Vik, T., Heinrich Schulz, Stéphane Allaire, et al.. (2008). Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6914. 69143G–69143G. 7 indexed citations
17.
Krücker, Jochen, Sheng Xu, Neil Glossop, et al.. (2007). Electromagnetic Tracking for Thermal Ablation and Biopsy Guidance: Clinical Evaluation of Spatial Accuracy. Journal of Vascular and Interventional Radiology. 18(9). 1141–1150. 161 indexed citations
18.
Schulz, Heinrich, et al.. (2005). Real-Time Interactive Viewing of 4D Kinematic MR Joint Studies. Lecture notes in computer science. 8(Pt 1). 467–473. 1 indexed citations
19.
Berg, Jens von, et al.. (2004). A hybrid method for registration of interventional CT and ultrasound images. International Congress Series. 1268. 492–497. 12 indexed citations
20.
Kösling, S., et al.. (1993). Knöcherne Variationen im koronaren Nasennebenhöhlen-CT. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 159(12). 506–510. 11 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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