İpek Oğuz

3.7k total citations · 1 hit paper
118 papers, 2.6k citations indexed

About

İpek Oğuz is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, İpek Oğuz has authored 118 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Radiology, Nuclear Medicine and Imaging, 47 papers in Computer Vision and Pattern Recognition and 14 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in İpek Oğuz's work include Medical Image Segmentation Techniques (33 papers), Advanced Neuroimaging Techniques and Applications (29 papers) and Advanced MRI Techniques and Applications (14 papers). İpek Oğuz is often cited by papers focused on Medical Image Segmentation Techniques (33 papers), Advanced Neuroimaging Techniques and Applications (29 papers) and Advanced MRI Techniques and Applications (14 papers). İpek Oğuz collaborates with scholars based in United States, Canada and Germany. İpek Oğuz's co-authors include Martin Styner, Guido Gerig, Dimitrios Pantazis, Shun Xu, Martha E. Shenton, James J. Levitt, François Budin, Fulton T. Crews, Milan Sonka and Leon G. Coleman and has published in prestigious journals such as PLoS ONE, NeuroImage and American Journal of Respiratory and Critical Care Medicine.

In The Last Decade

İpek Oğuz

117 papers receiving 2.5k citations

Hit Papers

Framework for the Statistical Shape Analysis of Brain Str... 2006 2026 2012 2019 2006 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
İpek Oğuz United States 24 886 540 402 387 255 118 2.6k
Meritxell Bach Cuadra Switzerland 28 1.4k 1.6× 660 1.2× 962 2.4× 350 0.9× 280 1.1× 145 3.4k
Arvid Lundervold Norway 29 1.2k 1.3× 1.0k 1.9× 890 2.2× 227 0.6× 247 1.0× 116 3.4k
Yonggang Shi United States 30 1.6k 1.8× 334 0.6× 743 1.8× 352 0.9× 344 1.3× 141 3.3k
Kilian M. Pohl United States 32 1.1k 1.2× 908 1.7× 887 2.2× 268 0.7× 195 0.8× 169 3.3k
Yangming Ou United States 24 1.1k 1.2× 578 1.1× 463 1.2× 338 0.9× 228 0.9× 95 2.5k
Vincent Frouin France 30 1.8k 2.0× 483 0.9× 982 2.4× 316 0.8× 238 0.9× 105 3.5k
Alexander Egan United States 5 1.9k 2.2× 655 1.2× 1.1k 2.7× 378 1.0× 286 1.1× 6 4.0k
Chunfeng Lian United States 31 975 1.1× 752 1.4× 397 1.0× 194 0.5× 461 1.8× 96 2.9k
Kelvin K. Leung United Kingdom 24 770 0.9× 459 0.8× 728 1.8× 123 0.3× 100 0.4× 45 2.6k
M GROSSMAN United States 5 1.6k 1.8× 845 1.6× 1.3k 3.4× 293 0.8× 332 1.3× 9 3.7k

Countries citing papers authored by İpek Oğuz

Since Specialization
Citations

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

Fields of papers citing papers by İpek Oğuz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of İpek Oğuz

This figure shows the co-authorship network connecting the top 25 collaborators of İpek Oğuz. A scholar is included among the top collaborators of İpek Oğuz 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 İpek Oğuz. İpek Oğuz 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.
Smith, Mariana Magnus, Robert J. Webster, İpek Oğuz, et al.. (2025). From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction. 21714–21720. 1 indexed citations
2.
Toubasi, Ahmad A., Jiacheng Wang, Bryan Hernandez, et al.. (2025). The Associations Between Chronic Active Lesions and White Matter Disease: A 7 Tesla Imaging Study. Annals of Clinical and Translational Neurology. 12(12). 2535–2545.
4.
Li, Hao, et al.. (2024). Novel OCT Mosaicking Pipeline with Feature-And Pixel-Based Registration. 1–5. 1 indexed citations
5.
Busch, Heinrich von, Robert Grimm, Henkjan Huisman, et al.. (2024). Deep Learning–based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets. Radiology Artificial Intelligence. 6(5). e230521–e230521. 3 indexed citations
6.
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
7.
Reed, Amy E. McCart, Natalie Pace, Amy N. Luckenbaugh, et al.. (2024). Automated Upper Tract Urothelial Carcinoma Tumor Segmentation During Ureteroscopy Using Computer Vision Techniques. Journal of Endourology. 38(8). 836–842. 4 indexed citations
9.
Oğuz, İpek, et al.. (2023). Towards navigation in endoscopic kidney surgery based on preoperative imaging. Healthcare Technology Letters. 11(2-3). 67–75. 2 indexed citations
10.
Wu, Jie Ying, et al.. (2023). ASSIST‐U: A system for segmentation and image style transfer for ureteroscopy. Healthcare Technology Letters. 11(2-3). 40–47. 1 indexed citations
11.
Hu, Dewei, et al.. (2023). Deep angiogram: trivializing retinal vessel segmentation. 112–112. 1 indexed citations
12.
Li, Hao, et al.. (2022). Cats: Complementary CNN and Transformer Encoders for Segmentation. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1–5. 8 indexed citations
13.
Oğuz, İpek, et al.. (2022). Computer Vision Enabled Segmentation of Kidney Stones During Ureteroscopy and Laser Lithotripsy. Journal of Endourology. 37(4). 495–501. 4 indexed citations
14.
Oğuz, İpek, et al.. (2020). Self-fusion for OCT noise reduction. PubMed. 11313. 11–11. 9 indexed citations
15.
Bakshi, Rohit, et al.. (2020). Robust Multiple Sclerosis Lesion Inpainting with Edge Prior. Lecture notes in computer science. 12436. 120–129. 7 indexed citations
16.
Dworkin, Jordan D., Kristin A. Linn, İpek Oğuz, et al.. (2018). An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions. American Journal of Neuroradiology. 39(4). 626–633. 27 indexed citations
17.
Oğuz, İpek, Satyananda Kashyap, Hongzhi Wang, Paul A. Yushkevich, & Milan Sonka. (2016). Globally Optimal Label Fusion with Shape Priors. Lecture notes in computer science. 9901. 538–546. 7 indexed citations
18.
Schuhmann, Maren, Youbing Yin, Daniela Gompelmann, et al.. (2015). Computed Tomography Predictors of Response to Endobronchial Valve Lung Reduction Treatment. Comparison with Chartis. American Journal of Respiratory and Critical Care Medicine. 191(7). 767–774. 82 indexed citations
19.
McMurray, Matthew S., İpek Oğuz, Ashley Rumple, et al.. (2014). Effects of prenatal cocaine exposure on early postnatal rodent brain structure and diffusion properties. Neurotoxicology and Teratology. 47. 80–88. 8 indexed citations
20.
Coleman, Leon G., İpek Oğuz, Joohwi Lee, Martin Styner, & Fulton T. Crews. (2012). Postnatal day 7 ethanol treatment causes persistent reductions in adult mouse brain volume and cortical neurons with sex specific effects on neurogenesis. Alcohol. 46(6). 603–612. 50 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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