Igor Gyacskov

438 total citations
18 papers, 328 citations indexed

About

Igor Gyacskov is a scholar working on Biomedical Engineering, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Igor Gyacskov has authored 18 papers receiving a total of 328 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Biomedical Engineering, 10 papers in Pulmonary and Respiratory Medicine and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Igor Gyacskov's work include Medical Imaging and Analysis (8 papers), Prostate Cancer Diagnosis and Treatment (7 papers) and Medical Image Segmentation Techniques (7 papers). Igor Gyacskov is often cited by papers focused on Medical Imaging and Analysis (8 papers), Prostate Cancer Diagnosis and Treatment (7 papers) and Medical Image Segmentation Techniques (7 papers). Igor Gyacskov collaborates with scholars based in Canada, China and United Kingdom. Igor Gyacskov's co-authors include Aaron Fenster, Cesare Romagnoli, Derek W. Cool, David D’Souza, Anthony Landry, C. Blake, J. David Spence, Mingyue Ding, Lori Gardi and Aaron D. Ward and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Igor Gyacskov

17 papers receiving 321 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Igor Gyacskov Canada 9 141 141 132 109 49 18 328
Juergen Weese Germany 11 73 0.5× 171 1.2× 127 1.0× 219 2.0× 66 1.3× 38 443
Vincent Daanen France 11 174 1.2× 203 1.4× 159 1.2× 155 1.4× 46 0.9× 15 427
Shi Sherebrin Canada 10 127 0.9× 117 0.8× 131 1.0× 113 1.0× 106 2.2× 14 325
Marcus Pfister Germany 10 116 0.8× 69 0.5× 144 1.1× 84 0.8× 90 1.8× 27 307
Milo Hindennach Germany 8 89 0.6× 162 1.1× 63 0.5× 140 1.3× 58 1.2× 12 293
Fei Mao United States 9 177 1.3× 244 1.7× 83 0.6× 141 1.3× 23 0.5× 14 399
Miguel Castro France 10 150 1.1× 40 0.3× 98 0.7× 60 0.6× 69 1.4× 33 270
Ole Vegard Solberg Norway 8 52 0.4× 144 1.0× 167 1.3× 157 1.4× 141 2.9× 16 417
Junko Tokuno Japan 11 102 0.7× 86 0.6× 39 0.3× 54 0.5× 75 1.5× 37 262
Timothy Hodges United States 5 241 1.7× 83 0.6× 93 0.7× 116 1.1× 168 3.4× 9 475

Countries citing papers authored by Igor Gyacskov

Since Specialization
Citations

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

Fields of papers citing papers by Igor Gyacskov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Igor Gyacskov

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

All Works

18 of 18 papers shown
1.
2.
Gyacskov, Igor, et al.. (2024). Deep learning for synovial volume segmentation of the first carpometacarpal joint in osteoarthritis patients. 4(1). 100176–100176. 3 indexed citations
3.
Gyacskov, Igor, José A. Gómez, David D’Souza, et al.. (2023). Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy. Brachytherapy. 22(2). 199–209. 1 indexed citations
4.
Gyacskov, Igor, et al.. (2023). Improving three-dimensional automated breast ultrasound resolution with orthogonal images. 3–3. 1 indexed citations
5.
Gyacskov, Igor, et al.. (2022). Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee. SHILAP Revista de lepidopterología. 4(3). 100290–100290. 13 indexed citations
6.
Gyacskov, Igor, Fumin Guo, Cesare Romagnoli, et al.. (2022). Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound. Physics in Medicine and Biology. 67(7). 74002–74002. 26 indexed citations
7.
Gyacskov, Igor, et al.. (2020). Deep learning segmentation of general interventional tools in two‐dimensional ultrasound images. Medical Physics. 47(10). 4956–4970. 33 indexed citations
8.
Gyacskov, Igor, et al.. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics. 47(6). 2413–2426. 59 indexed citations
9.
Sheikh, Khadija, Igor Gyacskov, Marco Mura, et al.. (2015). Three-dimensional segmentation of pulmonary artery volume from thoracic computed tomography imaging. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9417. 94172O–94172O. 1 indexed citations
10.
Buchanan, Daniel, et al.. (2012). Semi-automated segmentation of carotid artery total plaque volume from three dimensional ultrasound carotid imaging. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8317. 83170I–83170I. 9 indexed citations
11.
Fenster, Aaron, Derek W. Cool, Lori Gardi, et al.. (2010). Assessment of image registration accuracy in three‐dimensional transrectal ultrasound guided prostate biopsy. Medical Physics. 37(2). 802–813. 61 indexed citations
12.
Fenster, Aaron, et al.. (2010). Evaluation of Inter-session 3D-TRUS to 3D-TRUS Image Registration for Repeat Prostate Biopsies. Lecture notes in computer science. 13(Pt 2). 17–25. 4 indexed citations
13.
Fenster, Aaron, Derek W. Cool, Lori Gardi, et al.. (2010). Assessment of registration accuracy in three-dimensional transrectal ultrasound images of prostates. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7625. 762516–762516. 5 indexed citations
14.
Ding, Mingyue, Bernard Chiu, Igor Gyacskov, et al.. (2007). Fast prostate segmentation in 3D TRUS images based on continuity constraint using an autoregressive model. Medical Physics. 34(11). 4109–4125. 34 indexed citations
15.
Fenster, Aaron, C. Blake, Igor Gyacskov, Anthony Landry, & J. David Spence. (2006). 3D ultrasound analysis of carotid plaque volume and surface morphology. Ultrasonics. 44. e153–e157. 50 indexed citations
16.
Ding, Mingyue, et al.. (2005). Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint. PubMed. 2006. 662–665. 6 indexed citations
17.
Ding, Mingyue, et al.. (2004). Slice-based prostate segmentation in 3D US images based on continuity constraint. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5367. 151–151. 6 indexed citations
18.
Ding, Mingyue, Congjin Chen, Yunqiu Wang, Igor Gyacskov, & Aaron Fenster. (2003). Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5029. 69–69. 16 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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