Mirabela Rusu

5.8k total citations
84 papers, 1.2k citations indexed

About

Mirabela Rusu is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mirabela Rusu has authored 84 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Pulmonary and Respiratory Medicine, 45 papers in Radiology, Nuclear Medicine and Imaging and 21 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mirabela Rusu's work include Prostate Cancer Diagnosis and Treatment (43 papers), Radiomics and Machine Learning in Medical Imaging (38 papers) and AI in cancer detection (16 papers). Mirabela Rusu is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (43 papers), Radiomics and Machine Learning in Medical Imaging (38 papers) and AI in cancer detection (16 papers). Mirabela Rusu collaborates with scholars based in United States, United Kingdom and Denmark. Mirabela Rusu's co-authors include Willy Wriggers, Stefan Birmanns, Geoffrey A. Sonn, Richard E. Fan, Anant Madabhushi, Wei Shao, Pejman Ghanouni, Simon John Christoph Soerensen, Robert J. Graves and Yousef Al‐Kofahi and has published in prestigious journals such as Bioinformatics, Scientific Reports and The FASEB Journal.

In The Last Decade

Mirabela Rusu

78 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mirabela Rusu United States 20 516 425 290 211 182 84 1.2k
Anthony Sisk United States 23 589 1.1× 1.1k 2.7× 358 1.2× 204 1.0× 206 1.1× 79 2.0k
Nicholas P. Reder United States 16 157 0.3× 123 0.3× 137 0.5× 42 0.2× 407 2.2× 34 1.0k
Vijay Rajagopal Australia 19 216 0.4× 92 0.2× 65 0.2× 91 0.4× 301 1.7× 82 959
Stefan Wörz Germany 17 172 0.3× 214 0.5× 37 0.1× 296 1.4× 132 0.7× 68 978
Lionel Hervé France 15 341 0.7× 94 0.2× 63 0.2× 49 0.2× 393 2.2× 63 680
Kevin de Haan United States 17 172 0.3× 52 0.1× 465 1.6× 404 1.9× 422 2.3× 37 1.6k
Navid Farahani United States 11 261 0.5× 59 0.1× 461 1.6× 163 0.8× 98 0.5× 17 823
G. Raso Italy 19 315 0.6× 193 0.5× 269 0.9× 191 0.9× 275 1.5× 71 955
Adam M. Zysk United States 17 549 1.1× 133 0.3× 79 0.3× 64 0.3× 1.1k 6.3× 37 1.5k
Stina Svensson Sweden 15 375 0.7× 224 0.5× 54 0.2× 324 1.5× 134 0.7× 69 1.1k

Countries citing papers authored by Mirabela Rusu

Since Specialization
Citations

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

Fields of papers citing papers by Mirabela Rusu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mirabela Rusu

This figure shows the co-authorship network connecting the top 25 collaborators of Mirabela Rusu. A scholar is included among the top collaborators of Mirabela Rusu 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 Mirabela Rusu. Mirabela Rusu 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.
Zhou, Steve, Li‐Chun Zhang, Moon Hyung Choi, et al.. (2025). ProMUS ‐ NET : Artificial intelligence detects more prostate cancer than urologists on micro‐ultrasonography. British Journal of Urology. 136(6). 1071–1079.
2.
Li, Cynthia, Indrani Bhattacharya, Sulaiman Vesal, et al.. (2025). ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases. Medical Image Analysis. 101. 103486–103486. 1 indexed citations
3.
Rusu, Mirabela, Sulaiman Vesal, Cynthia Li, et al.. (2025). ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies. European Urology Oncology. 8(2). 477–485.
4.
Zhang, Lichun, Steve Zhou, Moon Hyung Choi, et al.. (2024). Deep learning for prostate and central gland segmentation on micro-ultrasound images. 30. 5–5. 2 indexed citations
5.
Rezaii, Paymon G., Daniel B. Herrick, Seth Tigchelaar, et al.. (2024). Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study. Neurospine. 21(2). 620–632. 1 indexed citations
6.
Soerensen, Simon John Christoph, Hriday P. Bhambhvani, Richard E. Fan, et al.. (2024). External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens. British Journal of Urology. 135(1). 133–139. 5 indexed citations
7.
Bitton, Rachel R., et al.. (2024). Intraprocedural Diffusion-weighted Imaging for Predicting Ablation Zone during MRI-guided Focused Ultrasound of Prostate Cancer. Radiology Imaging Cancer. 6(5). e240009–e240009. 2 indexed citations
8.
Vesal, Sulaiman, Indrani Bhattacharya, Xinran Li, et al.. (2024). A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images. 26–26. 1 indexed citations
9.
Shao, Wei, Sulaiman Vesal, Simon John Christoph Soerensen, et al.. (2024). RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate. Computers in Biology and Medicine. 173. 108318–108318. 8 indexed citations
10.
Shen, Xiaotao, Wei Shao, Chuchu Wang, et al.. (2022). Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine. Briefings in Bioinformatics. 23(5). 9 indexed citations
11.
Fu, Yunguan, Vasilis Stavrinides, Zachary M. C. Baum, et al.. (2022). Image quality assessment for machine learning tasks using meta-reinforcement learning. Medical Image Analysis. 78. 102427–102427. 28 indexed citations
12.
Vesal, Sulaiman, Indrani Bhattacharya, Shyam Natarajan, et al.. (2022). Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study. Medical Image Analysis. 82. 102620–102620. 22 indexed citations
13.
14.
Bhattacharya, Indrani, Leo C. Chen, Christian A. Kunder, et al.. (2021). Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Medical Physics. 48(6). 2960–2972. 41 indexed citations
15.
Shao, Wei, Christian A. Kunder, Richard E. Fan, et al.. (2020). ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate. Medical Image Analysis. 68. 101919–101919. 61 indexed citations
16.
Al‐Kofahi, Yousef, et al.. (2018). A deep learning-based algorithm for 2-D cell segmentation in microscopy images. BMC Bioinformatics. 19(1). 365–365. 138 indexed citations
17.
Ghose, Soumya, Rakesh Shiradkar, Mirabela Rusu, et al.. (2017). Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings. Scientific Reports. 7(1). 15829–15829. 9 indexed citations
18.
19.
Viswanath, Satish E., Róbert Tóth, Mirabela Rusu, et al.. (2014). Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer. Neurocomputing. 144. 13–23. 12 indexed citations
20.
Rusu, Mirabela & Stefan Birmanns. (2009). Integrative Multi-Resolution Modeling of Pleiomorphic Systems. Biophysical Journal. 96(3). 411a–411a. 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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