Roland Opfer

1.1k total citations
54 papers, 759 citations indexed

About

Roland Opfer is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Roland Opfer has authored 54 papers receiving a total of 759 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Pathology and Forensic Medicine and 12 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Roland Opfer's work include Multiple Sclerosis Research Studies (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Medical Imaging Techniques and Applications (11 papers). Roland Opfer is often cited by papers focused on Multiple Sclerosis Research Studies (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Medical Imaging Techniques and Applications (11 papers). Roland Opfer collaborates with scholars based in Germany, Switzerland and United States. Roland Opfer's co-authors include Sven Schippling, Robert Schaback, Leevan Ling, Rafael Wiemker, Lothar Spies, Ann‐Christin Ostwaldt, Praveena Manogaran, Per Suppa, Maria Pia Sormani and Alexander Schlaefer and has published in prestigious journals such as SHILAP Revista de lepidopterología, Neurology and Scientific Reports.

In The Last Decade

Roland Opfer

51 papers receiving 719 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roland Opfer Germany 14 258 200 128 99 98 54 759
Binjie Qin China 14 351 1.4× 41 0.2× 31 0.2× 104 1.1× 158 1.6× 37 759
Jichao Zhao New Zealand 28 327 1.3× 16 0.1× 39 0.3× 122 1.2× 104 1.1× 107 2.8k
Z. Liang United States 18 556 2.2× 163 0.8× 10 0.1× 62 0.6× 57 0.6× 67 1.0k
Giovanna Guidoboni United States 22 464 1.8× 36 0.2× 90 0.7× 70 0.7× 14 0.1× 99 1.6k
Žiga Špiclin Slovenia 14 259 1.0× 55 0.3× 9 0.1× 127 1.3× 63 0.6× 53 714
Shen Zhao China 13 169 0.7× 57 0.3× 15 0.1× 32 0.3× 119 1.2× 60 590
Matthew McCormick United States 17 422 1.6× 13 0.1× 42 0.3× 244 2.5× 65 0.7× 54 1.0k
Mehul Sampat United States 16 272 1.1× 347 1.7× 7 0.1× 49 0.5× 246 2.5× 37 1.3k

Countries citing papers authored by Roland Opfer

Since Specialization
Citations

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

Fields of papers citing papers by Roland Opfer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roland Opfer

This figure shows the co-authorship network connecting the top 25 collaborators of Roland Opfer. A scholar is included among the top collaborators of Roland Opfer 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 Roland Opfer. Roland Opfer 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.
Krüger, Julia, et al.. (2025). Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs. Computers in Biology and Medicine. 186. 109660–109660. 4 indexed citations
2.
Cruciani, Carolina, Marco Puthenparampil, María José Docampo, et al.. (2025). Intrathecal Anti-Akkermansia muciniphila IgG Responses in Multiple Sclerosis Patients Linked to CSF Immune Cells and Disease Activity. Journal of Clinical Medicine. 14(16). 5771–5771.
3.
4.
Opfer, Roland, et al.. (2024). BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI. International Journal of Computer Assisted Radiology and Surgery. 19(9). 1763–1771. 1 indexed citations
5.
Schultz, Vivian, Dennis M. Hedderich, Benita Schmitz‐Koep, et al.. (2024). Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry. Neuroradiology. 66(4). 507–519. 4 indexed citations
6.
Opfer, Roland, et al.. (2024). Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning. Computers in Biology and Medicine. 183. 109289–109289. 1 indexed citations
7.
Opfer, Roland, et al.. (2024). Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation. 11599. 99–99. 1 indexed citations
8.
Apostolova, Ivayla, et al.. (2023). Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance. European Journal of Nuclear Medicine and Molecular Imaging. 51(5). 1333–1344. 4 indexed citations
9.
10.
Schlaeger, Sarah, Suprosanna Shit, Paul Eichinger, et al.. (2023). AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis. Insights into Imaging. 14(1). 123–123. 7 indexed citations
11.
Opfer, Roland, et al.. (2023). A systematic approach to deep learning-based nodule detection in chest radiographs. Scientific Reports. 13(1). 10120–10120. 8 indexed citations
12.
Opfer, Roland, Lothar Spies, Ann‐Christin Ostwaldt, et al.. (2022). Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability. European Radiology. 33(3). 1852–1861. 19 indexed citations
13.
Ostwaldt, Ann‐Christin, Lothar Spies, Benjamin Geisler, et al.. (2021). Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks. European Radiology. 32(4). 2798–2809. 13 indexed citations
14.
Gessert, Nils, Roland Opfer, Ann‐Christin Ostwaldt, et al.. (2020). 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation. 1 indexed citations
15.
Opfer, Roland, Nils Gessert, Ann‐Christin Ostwaldt, et al.. (2020). Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. NeuroImage Clinical. 28. 102445–102445. 43 indexed citations
16.
Opfer, Roland, Ann‐Christin Ostwaldt, Praveena Manogaran, et al.. (2018). Within-patient fluctuation of brain volume estimates from short-term repeated MRI measurements using SIENA/FSL. Journal of Neurology. 265(5). 1158–1165. 18 indexed citations
17.
Opfer, Roland, Ann‐Christin Ostwaldt, Maria Pia Sormani, et al.. (2017). Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL—a longitudinal brain volumetry study in healthy adults. Neurobiology of Aging. 65. 1–6. 23 indexed citations
18.
Egger, Christine, Roland Opfer, Chenyu Wang, et al.. (2016). MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?. NeuroImage Clinical. 13. 264–270. 84 indexed citations
19.
Spies, Lothar, et al.. (2013). Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis. Physics in Medicine and Biology. 58(23). 8323–8337. 9 indexed citations
20.
Opfer, Roland. (2005). Tight frame expansions of multiscale reproducing kernels in Sobolev spaces. Applied and Computational Harmonic Analysis. 20(3). 357–374. 18 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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