Saša Grbić

2.3k total citations
31 papers, 857 citations indexed

About

Saša Grbić is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Saša Grbić has authored 31 papers receiving a total of 857 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Pulmonary and Respiratory Medicine and 10 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Saša Grbić's work include COVID-19 diagnosis using AI (13 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Cardiac Valve Diseases and Treatments (9 papers). Saša Grbić is often cited by papers focused on COVID-19 diagnosis using AI (13 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Cardiac Valve Diseases and Treatments (9 papers). Saša Grbić collaborates with scholars based in United States, Germany and Switzerland. Saša Grbić's co-authors include Dorin Comaniciu, Bogdan Georgescu, Joachim Hornegger, Andreas Maier, Yefeng Zheng, Florin‐Cristian Ghesu, Tommaso Mansi, Guillaume Chabin, Zhoubing Xu and Ali Kamen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Transactions on Medical Imaging.

In The Last Decade

Saša Grbić

28 papers receiving 844 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Saša Grbić United States 16 496 205 183 177 174 31 857
Theresa Thai United States 12 519 1.0× 126 0.6× 170 0.9× 164 0.9× 331 1.9× 32 1.0k
Joshy Cyriac Switzerland 11 496 1.0× 131 0.6× 83 0.5× 271 1.5× 95 0.5× 22 789
Erik Smistad Norway 19 583 1.2× 155 0.8× 284 1.6× 205 1.2× 125 0.7× 53 1.1k
Michael Suehling Germany 14 365 0.7× 103 0.5× 212 1.2× 276 1.6× 137 0.8× 28 727
Soichiro Miki Japan 14 323 0.7× 203 1.0× 71 0.4× 94 0.5× 152 0.9× 51 659
Timothy J. W. Dawes United Kingdom 17 615 1.2× 168 0.8× 534 2.9× 227 1.3× 165 0.9× 39 1.5k
Jiwoong Jeong United States 12 244 0.5× 83 0.4× 95 0.5× 127 0.7× 168 1.0× 34 609
Teemu Mäkelä Finland 12 336 0.7× 98 0.5× 169 0.9× 197 1.1× 51 0.3× 44 689
Eranga Ukwatta Canada 19 540 1.1× 405 2.0× 410 2.2× 271 1.5× 126 0.7× 91 1.2k

Countries citing papers authored by Saša Grbić

Since Specialization
Citations

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

Fields of papers citing papers by Saša Grbić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saša Grbić

This figure shows the co-authorship network connecting the top 25 collaborators of Saša Grbić. A scholar is included among the top collaborators of Saša Grbić 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 Saša Grbić. Saša Grbić 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.
Xu, Zhoubing, et al.. (2024). COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training. IEEE Transactions on Medical Imaging. 43(5). 1995–2009. 15 indexed citations
2.
Mansoor, Awais, Florin C. Ghesu, Bogdan Georgescu, et al.. (2024). Large-Scale Study on AI’s Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging. Academic Radiology. 31(12). 5300–5313.
3.
Mansoor, Awais, et al.. (2023). AUCReshaping: improved sensitivity at high-specificity. Scientific Reports. 13(1). 21097–21097. 6 indexed citations
4.
Marschner, Sebastian, Manasi Datar, Zhoubing Xu, et al.. (2022). A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation. Radiation Oncology. 17(1). 129–129. 15 indexed citations
5.
Huch, R., et al.. (2021). Detection and characterization of COVID-19 findings in chest CT. Medicine. 100(41). e27478–e27478.
6.
Weikert, Thomas, Saikiran Rapaka, Saša Grbić, et al.. (2021). Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings. Korean Journal of Radiology. 22(6). 994–994. 13 indexed citations
7.
Barbosa, Eduardo J. Mortani, Bogdan Georgescu, Shikha Chaganti, et al.. (2021). Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort. PubMed Central. 17 indexed citations
8.
Seifert, Robert, Ken Herrmann, Jens Kleesiek, et al.. (2020). Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer. Journal of Nuclear Medicine. 61(12). 1786–1792. 91 indexed citations
9.
Chaganti, Shikha, Philippe Greniér, Guillaume Chabin, et al.. (2020). Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT. Radiology Artificial Intelligence. 2(4). e200048–e200048. 94 indexed citations
10.
Fischer, Andreas, Rock H. Savage, John Martinez, et al.. (2020). Machine Learning/Deep Neuronal Network. Journal of Thoracic Imaging. 35(Supplement 1). S21–S27. 17 indexed citations
11.
Liu, Siqi, Arnaud A. A. Setio, Florin C. Ghesu, et al.. (2020). No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks. IEEE Transactions on Medical Imaging. 40(1). 335–345. 36 indexed citations
12.
Ghesu, Florin C., Bogdan Georgescu, Saša Grbić, et al.. (2018). Towards intelligent robust detection of anatomical structures in incomplete volumetric data. Medical Image Analysis. 48. 203–213. 26 indexed citations
13.
Grbić, Saša, Tommaso Mansi, Charles H. Bloodworth, et al.. (2016). Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography. Medical Image Analysis. 35. 238–249. 15 indexed citations
14.
Neumann, Dominik, Saša Grbić, Matthias John, et al.. (2013). Robust Model-Based 3D/3D Fusion Using Sparse Matching for Minimally Invasive Surgery. Lecture notes in computer science. 16(Pt 1). 171–178. 1 indexed citations
15.
Grbić, Saša, Tommaso Mansi, Razvan Ionasec, et al.. (2013). Image-Based Computational Models for TAVI Planning: From CT Images to Implant Deployment. Lecture notes in computer science. 16(Pt 2). 395–402. 22 indexed citations
16.
Grbić, Saša, et al.. (2012). Model-based fusion of CT and non-contrasted 3D C-arm CT: Application to transcatheter valve therapies. 5636. 1192–1195. 1 indexed citations
17.
Grbić, Saša, Razvan Ionasec, Ingmar Voigt, et al.. (2012). Complete valvular heart apparatus model from 4D cardiac CT. Medical Image Analysis. 16(5). 1003–1014. 43 indexed citations
18.
Grbić, Saša, Razvan Ionasec, Yang Wang, et al.. (2011). Model-Based Fusion of Multi-modal Volumetric Images: Application to Transcatheter Valve Procedures. Lecture notes in computer science. 14(Pt 1). 219–226. 5 indexed citations
19.
Grbić, Saša, Razvan Ionasec, Ingmar Voigt, et al.. (2010). Complete Valvular Heart Apparatus Model from 4D Cardiac CT. Lecture notes in computer science. 13(Pt 1). 218–226. 16 indexed citations
20.
Grbić, Saša, et al.. (2010). Aortic valve and ascending aortic root modeling from 3D and 3D+t CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7625. 76250H–76250H.

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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