Jakob Wasserthal

3.9k total citations · 3 hit papers
23 papers, 1.2k citations indexed

About

Jakob Wasserthal is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Neurology. According to data from OpenAlex, Jakob Wasserthal has authored 23 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Biomedical Engineering and 4 papers in Neurology. Recurrent topics in Jakob Wasserthal's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Advanced Neuroimaging Techniques and Applications (6 papers) and Advanced MRI Techniques and Applications (5 papers). Jakob Wasserthal is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Advanced Neuroimaging Techniques and Applications (6 papers) and Advanced MRI Techniques and Applications (5 papers). Jakob Wasserthal collaborates with scholars based in Switzerland, Germany and Chile. Jakob Wasserthal's co-authors include Peter Neher, Klaus Maier‐Hein, Shan Yang, Daniel T. Boll, Hanns‐Christian Breit, Michael Bach, Alexander Sauter, Martin Segeroth, Joshy Cyriac and Tobias Heye and has published in prestigious journals such as PLoS ONE, NeuroImage and Radiology.

In The Last Decade

Jakob Wasserthal

20 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

2023 465 citations Jakob Wasserthal, Hanns‐Christian Breit et al. Radiology Artificial Intelligence profile →
TractSeg - Fast and accurate white matter tract segmentation

2018 373 citations Jakob Wasserthal, Peter Neher et al. NeuroImage profile →
TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

2025 26 citations Tugba Akinci D’Antonoli, Matthias Jung et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jakob Wasserthal Switzerland	10	867	215	157	157	118	23	1.2k
Peter Savadjiev United States	18	989 1.1×	126 0.6×	192 1.2×	308 2.0×	37 0.3×	50	1.3k
Karteek Popuri Canada	21	330 0.4×	114 0.5×	46 0.3×	130 0.8×	153 1.3×	69	1.4k
Joon Yul Choi South Korea	19	736 0.8×	107 0.5×	75 0.5×	78 0.5×	190 1.6×	68	1.2k
Woo Hyun Shim South Korea	23	1.0k 1.2×	179 0.8×	68 0.4×	164 1.0×	23 0.2×	103	1.9k
Alireza Akhondi‐Asl United States	16	300 0.3×	87 0.4×	323 2.1×	134 0.9×	181 1.5×	48	900
Peter Neher Germany	13	785 0.9×	69 0.3×	232 1.5×	225 1.4×	59 0.5×	35	997
Dinggang Shen United States	14	490 0.6×	144 0.7×	85 0.5×	207 1.3×	576 4.9×	27	1.2k
Oliver Sedlaczek Germany	17	401 0.5×	256 1.2×	96 0.6×	149 0.9×	28 0.2×	49	1.4k
Fabian Isensee Germany	15	578 0.7×	211 1.0×	33 0.2×	53 0.3×	222 1.9×	37	955
Atsushi K. Kono Japan	23	1.0k 1.2×	354 1.6×	42 0.3×	185 1.2×	54 0.5×	100	2.0k

Countries citing papers authored by Jakob Wasserthal

Since Specialization

Citations

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

Fields of papers citing papers by Jakob Wasserthal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jakob Wasserthal

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

All Works

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20 of 20 papers shown

Wasserthal, Jakob, Martin Segeroth, Shan Yang, et al.. (2025). Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI. Journal of Imaging Informatics in Medicine. 39(1). 345–354.

D’Antonoli, Tugba Akinci, Matthias Jung, Alexander Rau, et al.. (2025). TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI. Radiology. 314(2). e241613–e241613. 26 indexed citations breakdown →

Santini, Francesco, Jakob Wasserthal, Abramo Agosti, et al.. (2025). Deep Anatomical Federated Network (Dafne): An Open Client-Server Framework for Continuous, Collaborative Improvement of Deep Learning–based Medical Image Segmentation. Radiology Artificial Intelligence. 7(3). e240097–e240097. 2 indexed citations

Segeroth, Martin, et al.. (2025). Automatic Segmentation of Cardiovascular Structures on Chest CT Data Sets: An Update of the TotalSegmentator. European Journal of Radiology. 185. 112006–112006. 2 indexed citations

Bach, Michael, Jakob Wasserthal, Martin Segeroth, et al.. (2025). Liver Segment and Lesion Segmentation on CT and MRI: An Open-Source Contribution to TotalSegmentator. Journal of Imaging Informatics in Medicine.

Naghavi, Morteza, Anthony P. Reeves, Chenyu Zhang, et al.. (2024). AI-enabled Left Atrial Volumetry In Coronary Calcium Scans (AI-CAC) Predicts Atrial Fibrillation As Early As One Year, Improves Charge-AF, And Outperforms NT-proBNP: The Multi-ethnic Study Of Atherosclerosis. Journal of cardiovascular computed tomography. 18(4). S15–S15.

Naghavi, Morteza, David F. Yankelevitz, Anthony P. Reeves, et al.. (2024). AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis. Journal of cardiovascular computed tomography. 18(4). 383–391. 6 indexed citations

Wasserthal, Jakob, Jan Vosshenrich, Shan Yang, et al.. (2024). Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation—Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model. Journal of Imaging Informatics in Medicine. 38(3). 1617–1627. 1 indexed citations

Breit, Hanns‐Christian, Jakob Wasserthal, Michael Bach, et al.. (2024). AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort. Academic Radiology. 32(5). 2700–2706. 1 indexed citations

10.

Bumm, R., Paolo Zaffino, András Lassó, et al.. (2024). Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients. Journal of Thoracic Disease. 16(2). 1009–1020. 2 indexed citations

11.

Rusche, Thilo, Jakob Wasserthal, Hanns‐Christian Breit, et al.. (2023). Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage. Journal of Clinical Medicine. 12(7). 2631–2631. 4 indexed citations

12.

D’Antonoli, Tugba Akinci, et al.. (2023). Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans. Radiology Artificial Intelligence. 5(5). e220292–e220292. 6 indexed citations

13.

Rusche, Thilo, Hanns‐Christian Breit, Michael Bach, et al.. (2023). Prospective Assessment of Cerebral Microbleeds with Low-Field Magnetic Resonance Imaging (0.55 Tesla MRI). Journal of Clinical Medicine. 12(3). 1179–1179. 4 indexed citations

14.

Segeroth, Martin, David Winkel, Ivo Strebel, et al.. (2023). Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics. European Heart Journal - Cardiovascular Imaging. 24(8). 1062–1071. 4 indexed citations

15.

Wasserthal, Jakob, Thomas Weikert, Alexander Sauter, et al.. (2021). Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning. Diagnostics. 11(5). 901–901. 29 indexed citations

16.

Barnett, Michael, Jakob Wasserthal, Con Yiannikas, et al.. (2021). Differentiating axonal loss and demyelination in chronic MS lesions: A novel approach using single streamline diffusivity analysis. PLoS ONE. 16(1). e0244766–e0244766. 9 indexed citations

17.

Wasserthal, Jakob, Klaus Maier‐Hein, Peter Neher, et al.. (2021). White matter microstructure alterations in cortico-striatal networks are associated with parkinsonism in schizophrenia spectrum disorders. European Neuropsychopharmacology. 50. 64–74. 11 indexed citations

18.

Wasserthal, Jakob, Klaus Maier‐Hein, Peter Neher, et al.. (2020). Multiparametric mapping of white matter microstructure in catatonia. Neuropsychopharmacology. 45(10). 1750–1757. 61 indexed citations

19.

Wasserthal, Jakob, Peter Neher, Dušan Hirjak, & Klaus Maier‐Hein. (2019). Combined tract segmentation and orientation mapping for bundle-specific tractography. Medical Image Analysis. 58. 101559–101559. 116 indexed citations

20.

Wasserthal, Jakob, Peter Neher, & Klaus Maier‐Hein. (2018). TractSeg - Fast and accurate white matter tract segmentation. NeuroImage. 183. 239–253. 373 indexed citations breakdown →

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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