Last Updated: May 2025
Peer-Reviewed Articles
2025
| LINK | LiMostafa, A. A., Wu, Q., Berman, J. R., Hirshberg, R. M., Walker, E. D., Barr, C. A., & Ukwatta, E. (2024). Deep learning for the detection of cervical spondylomyelopathy in Doberman Pinschers using lateral radiographs. American Journal of Veterinary Research. 2024 Apr; aop:ajvr.24.08.0248. | |
2024
| LINK | Yu, Z., Sun, Y., Mostafa, A. A., White, J. A., & Ukwatta, E. (2024). Ventricle segmentation in infant brain ultrasound using transformer-based networks. Biomedical Physics & Engineering Express. 2024 Apr;10(2):025024. | |
2023
| LINK | Liu, D., Ukwatta, E., Quirk, J. D., Hu, Y., Dharmakumar, R., & White, J. A. (2023). Automatic myocardial segmentation from T1 mapping cardiovascular magnetic resonance images using convolutional neural networks. Medical Physics. 2023 Mar;50(6), 3973–3984. | |
2022
| LINK | Farrag NA, Bhagavan S, Sebben D, Ruwanpura P, White JA, Ukwatta E. Automated myocardial segmentation of extra-cellular volume mapping cardiac magnetic resonance images using fully convolutional neural networks. InMedical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022 Apr 4 (Vol. 12036, pp. 602-609). SPIE. | |
| LINK | Szentimrey Z, de Ribaupierre S, Fenster A, Ukwatta E. Automated 3D U‐net based segmentation of neonatal cerebral ventricles from 3D ultrasound images. Medical physics. 2022 Feb;49(2):1034-46. |
2021
| LINK | Wong T, Schieda N, Sathiadoss P, Haroon M, Abreu‐Gomez J, Ukwatta E. Fully automated detection of prostate transition zone tumors on T2‐weighted and apparent diffusion coefficient (ADC) map MR images using U‐Net ensemble. Medical Physics. 2021 Nov;48(11):6889-900. | |
| LINK | Manokaran J, Zabihollahy F, Hamilton-Wright A, Ukwatta E. Detection of COVID-19 from chest x-ray images using transfer learning. Journal of Medical Imaging. 2021 Aug;8(S1):017503. | |
Conference Papers
2025
| LINK | Zhang Y., Sun Y., Lu Y., Qi J., Ukwatta E., “Scribble-based weakly supervised method for segmentation of neonatal cerebral ventricles in ultrasound images,” SPIE Medical Imaging, 13412, 1–7, 2025. | |
2022
| Gaikar, R., Zabihollahy, F., Elfaal, M. W., Schieda, N., & Ukwatta, E. “Transfer learning based fully automated kidney segmentation on MR images,” SPIE Medical Imaging, 1-7, USA, 2022 | ||
| Farrag N. A., Bhagavan S., Sebben D., Ruwanpura P., White J. A., Ukwatta E., “Automated myocardial segmentation of extra-cellular volume mapping cardiac magnetic resonance images using fully convolutional neural networks,” SPIE Medical Imaging, 1-8, 2022 |
2021
| Manokaran J., *Zabihollahy F., Hamilton-Wright A., Ukwatta E., “Deep learning-based detection of COVID-19 from Chest X-ray images,” SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1-7, USA, 2021 | ||
| Manokaran J, Zabihollahy F, Hamilton-Wright A, Ukwatta E. Detection of COVID-19 from chest x-ray images using transfer learning. Journal of Medical Imaging. 2021 Aug;8(S1):017503. | ||
| Alzahrani A., Hosseinkhani J., Rajan S., Ukwatta E., Reducing Motion Impact on Video Magnification Using Wavelet Transform and Principal Component Analysis for Heart Rate Estimation, IEEE International Instrumentation and Measurement Technology Conference | ||
| Szentimrey Z., Ribaupierre S., Fenster A., Ukwatta E., “Automatic deep learning based segmentation of neonatal cerebral ventricles from 3D ultrasound images,” SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1-7, San Diego, USA 2021 | ||