| Automated 3D U‐net based segmentation of neonatal cerebral ventricles from 3D ultrasound images Z Szentimrey, S de Ribaupierre, A Fenster, E Ukwatta Medical Physics 49 (2), 1034-1046, 2022 | 18 | 2022 |
| Automatic deep learning-based segmentation of neonatal cerebral ventricles from 3D ultrasound images Z Szentimrey, S de Ribaupierre, A Fenster, E Ukwatta Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and …, 2021 | 9 | 2021 |
| Automated segmentation and measurement of the female pelvic floor from the mid‐sagittal plane of 3D ultrasound volumes Z Szentimrey, G Ameri, CX Hong, RYK Cheung, E Ukwatta, A Eltahawi Medical Physics 50 (10), 6215-6227, 2023 | 3 | 2023 |
| Automated Deep Learning Segmentation of Neonatal Cerebral Lateral Ventricles from Three-Dimensional Ultrasound Images Z Szentimrey University of Guelph, 2021 | 2 | 2021 |
| Semi‐supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound Z Szentimrey, A Al‐Hayali, S de Ribaupierre, A Fenster, E Ukwatta Medical Physics, 2024 | | 2024 |
| AUTOMATIC SEGMENTATION OF PELVIC ORGANS IN 3D TRANSPERINEAL ULTRASOUND USING DEEP LEARNING Z Szentimrey, G Ameri, C Hong, R Cheung, E Ukwatta, A Eltahawi Continence 7, 100997, 2023 | | 2023 |
| Automated segmentation and measurement of the levator hiatus in 3D transperineal ultrasound Z Szentimrey, G Ameri, RYK Cheung, A Eltahawi, E Ukwatta Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and …, 2023 | | 2023 |
| Segmentation of Neonatal Cerebral Lateral Ventricles from 3D Ultrasound Images Z Szentimrey, E Ukwatta 3D Ultrasound, 193-208, 0 | | |
