Tomasz Morgaś

440 total citations
11 papers, 151 citations indexed

About

Tomasz Morgaś is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Artificial Intelligence. According to data from OpenAlex, Tomasz Morgaś has authored 11 papers receiving a total of 151 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Radiation and 3 papers in Artificial Intelligence. Recurrent topics in Tomasz Morgaś's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Advanced Radiotherapy Techniques (6 papers) and Medical Imaging Techniques and Applications (5 papers). Tomasz Morgaś is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Advanced Radiotherapy Techniques (6 papers) and Medical Imaging Techniques and Applications (5 papers). Tomasz Morgaś collaborates with scholars based in Switzerland, Germany and United States. Tomasz Morgaś's co-authors include Benjamin de Haas, Tobias Gass, Michael Schwier, Hannu Laaksonen, Jan Klein, Horst K. Hahn, Jan Hendrik Moltz, Benjamin Geisler, Volker Dicken and Hans Meine and has published in prestigious journals such as Radiotherapy and Oncology, Journal of Medical Imaging and Reports of Practical Oncology & Radiotherapy.

In The Last Decade

Tomasz Morgaś

11 papers receiving 150 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Tomasz Morgaś	102	70	60	42	25	11	151
Bowman Williams	126 1.2×	70 1.0×	41 0.7×	33 0.8×	13 0.5×	5	173
Haoyu Zhong	212 2.1×	49 0.7×	73 1.2×	45 1.1×	30 1.2×	21	254
Sadegh Riyahi	217 2.1×	62 0.9×	58 1.0×	119 2.8×	32 1.3×	9	257
Xudong Xue	155 1.5×	65 0.9×	65 1.1×	46 1.1×	44 1.8×	26	245
Stéphane Niyoteka	187 1.8×	27 0.4×	63 1.1×	23 0.5×	21 0.8×	10	222
Kamal Singhrao	205 2.0×	149 2.1×	91 1.5×	37 0.9×	39 1.6×	16	249
Enzo Battistella	208 2.0×	24 0.3×	76 1.3×	47 1.1×	34 1.4×	14	276
Émilie Alvarez Andres	182 1.8×	24 0.3×	66 1.1×	21 0.5×	32 1.3×	5	223
Y. M. Goh	73 0.7×	55 0.8×	19 0.3×	27 0.6×	13 0.5×	14	99
G.G. Sikkes	197 1.9×	208 3.0×	59 1.0×	115 2.7×	14 0.6×	8	256

Countries citing papers authored by Tomasz Morgaś

Since Specialization

Citations

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

Fields of papers citing papers by Tomasz Morgaś

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomasz Morgaś

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

All Works

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11 of 11 papers shown

Moltz, Jan Hendrik, et al.. (2020). Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours. Journal of Medical Imaging. 7(6). 64001–64001. 9 indexed citations

Moltz, Jan Hendrik, et al.. (2020). Learning a Loss Function for Segmentation: A Feasibility Study. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 357–360. 10 indexed citations

Papenberg, Nils, et al.. (2020). Deep learning based CT-CBCT image registration for adaptive radio therapy. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 25–25. 2 indexed citations

Laaksonen, Hannu, et al.. (2019). Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT. Radiotherapy and Oncology. 145. 1–6. 45 indexed citations

Dicken, Volker, et al.. (2019). Quantitative and qualitative methods for efficient evaluation of multiple 3D organ segmentations. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 9351. 39–39. 1 indexed citations

Dicken, Volker, et al.. (2019). Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning. 28–28. 8 indexed citations

Morgaś, Tomasz, et al.. (2018). PV-0530: Parotid gland segmentation with deep learning using clinical vs. curated training data. Radiotherapy and Oncology. 127. S281–S282. 4 indexed citations

Schwier, Michael, et al.. (2018). Evaluation of deep learning methods for parotid gland segmentation from CT images. Journal of Medical Imaging. 6(1). 1–1. 35 indexed citations

Schwier, Michael, Tomasz Morgaś, Jan Klein, et al.. (2018). Comparison of different deep learning approaches for parotid gland segmentation from CT images. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 11 indexed citations

10.

Morgaś, Tomasz, et al.. (2009). The gamma evaluation method as a routine QA procedure of IMRT. Reports of Practical Oncology & Radiotherapy. 14(5). 162–168. 24 indexed citations

11.

Morgaś, Tomasz, et al.. (2006). Radiation treatment planning using positron emission tomography for patients with non-small cell lung cancer. Nowotwory Journal of Oncology. 56(3). 259–259. 2 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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