Gyacskov I - Search Results

1

Three-dimensional complementary breast ultrasound (3D CBUS): Improving 3D spatial resolution uniformity with orthogonal images.

Park CKS, Aziz A, Trumpour T, Bax JS, Tessier D, Gyacskov I, Gardi L, Fenster A. Park CKS, et al. Among authors: gyacskov i. Med Phys. 2025 Jan 22. doi: 10.1002/mp.17626. Online ahead of print. Med Phys. 2025. PMID: 39844441

2

Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy.

Orlando N, Edirisinghe C, Gyacskov I, Vickress J, Sachdeva R, Gomez JA, D'Souza D, Velker V, Mendez LC, Bauman G, Fenster A, Hoover DA. Orlando N, et al. Among authors: gyacskov i. Brachytherapy. 2023 Mar-Apr;22(2):199-209. doi: 10.1016/j.brachy.2022.11.011. Epub 2023 Jan 12. Brachytherapy. 2023. PMID: 36641305

3

Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee.

du Toit C, Orlando N, Papernick S, Dima R, Gyacskov I, Fenster A. du Toit C, et al. Among authors: gyacskov i. Osteoarthr Cartil Open. 2022 Jun 25;4(3):100290. doi: 10.1016/j.ocarto.2022.100290. eCollection 2022 Sep. Osteoarthr Cartil Open. 2022. PMID: 36474947 Free PMC article.

4

Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound.

Orlando N, Gyacskov I, Gillies DJ, Guo F, Romagnoli C, D'Souza D, Cool DW, Hoover DA, Fenster A. Orlando N, et al. Among authors: gyacskov i. Phys Med Biol. 2022 Mar 29;67(7). doi: 10.1088/1361-6560/ac5a93. Phys Med Biol. 2022. PMID: 35240585

5

Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

Gillies DJ, Rodgers JR, Gyacskov I, Roy P, Kakani N, Cool DW, Fenster A. Gillies DJ, et al. Among authors: gyacskov i. Med Phys. 2020 Oct;47(10):4956-4970. doi: 10.1002/mp.14427. Epub 2020 Aug 30. Med Phys. 2020. PMID: 32767411

6

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

Orlando N, Gillies DJ, Gyacskov I, Romagnoli C, D'Souza D, Fenster A. Orlando N, et al. Among authors: gyacskov i. Med Phys. 2020 Jun;47(6):2413-2426. doi: 10.1002/mp.14134. Epub 2020 Apr 8. Med Phys. 2020. PMID: 32166768

7

Evaluation of inter-session 3D-TRUS to 3D-TRUS image registration for repeat prostate biopsies.

Karnik VV, Fenster A, Bax J, Gardi L, Gyacskov I, Montreuil J, Romagnoli C, Ward AD. Karnik VV, et al. Among authors: gyacskov i. Med Image Comput Comput Assist Interv. 2010;13(Pt 2):17-25. doi: 10.1007/978-3-642-15745-5_3. Med Image Comput Comput Assist Interv. 2010. PMID: 20879294

8

Assessment of image registration accuracy in three-dimensional transrectal ultrasound guided prostate biopsy.

Karnik VV, Fenster A, Bax J, Cool DW, Gardi L, Gyacskov I, Romagnoli C, Ward AD. Karnik VV, et al. Among authors: gyacskov i. Med Phys. 2010 Feb;37(2):802-13. doi: 10.1118/1.3298010. Med Phys. 2010. PMID: 20229890

9

Fast prostate segmentation in 3D TRUS images based on continuity constraint using an autoregressive model.

Ding M, Chiu B, Gyacskov I, Yuan X, Drangova M, Downey DB, Fenster A. Ding M, et al. Among authors: gyacskov i. Med Phys. 2007 Nov;34(11):4109-25. doi: 10.1118/1.2777005. Med Phys. 2007. PMID: 18072477

10

Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint.

Ding M, Gyacskov I, Yuan X, Drangova M, Fenster A, Downey D. Ding M, et al. Among authors: gyacskov i. Conf Proc IEEE Eng Med Biol Soc. 2005;2006:662-5. doi: 10.1109/IEMBS.2005.1616500. Conf Proc IEEE Eng Med Biol Soc. 2005. PMID: 17282269

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