Robert Rohling

Robert Rohling

Robert Rohling

Professor and Director ICICS

P.Eng., B.A.Sc. (UBC), M.Eng. (McGill), Ph.D. (Cambridge)

phone: (604) 822-2045
fax: (604) 822-2403
email: rohling@ece.ubc.ca
website:  Robotics and Control Laboratory
office: KAIS 3059

Research Interests

  • Biomedical Engineering
  • Medical Imaging
  • Medical Information Systems
  • Robotics

Current Research Work

  • In medical imaging, I am developing new acquisition techniques for ultrasound with the goal of improving diagnostics. Two current research directions in this area are 3D ultrasound and spatial compounding to improve the visualization of anatomy and pathology.
  • In medical information systems, I am working with industry to improve the timely dissemination of digital medical images and associated data to health care providers. In particular I am currently working on new methods for radiologists to navigate large image sets. This is in response to the growing size of studies produced by modern scanners.
  • Finally, I have an interest in the calibration of robotic systems and their application in surgery.

All of these topics are multidisciplinary and I hold a joint appointment with the Department of Electrical and Computer Engineering and the Department of Mechanical Engineering to support this research.

Selected Publications

  • M. Pesteie, V. Lessoway, P. Abolmaesumi, R. Rohling. Automatic midline identification in transverse 2D ultrasound images of the spine. Ultrasound in Medicine and Biology. 46(1), pp. 2846-2854, 2020.
  • M. Jafari, H. Girgis, N. van Woudenberg, N. Moulson, C. Luong, A. Fung, S. Balthazaar, J. Jue, M. Tsang, P. Nair, K. Gin, R. Rohling, P. Abolmaesumi, T. Tsang. Cardiac point-of-care to cart-based ultrasound translation using constrained cycleGAN. International Journal of Computer Assisted Radiology and Surgery. 15, pp. 877-886, 2020.
  • L. Porto, R. Tang, A. Sawka, V. Lessoway, E. Abu Anas, D. Behnami, P. Abolmaesumi, R. Rohling. A comparative study on position and paramedian neuraxial access on healthy volunteers using 3D models registered to lumbar spine ultrasound. Canadian Journal of Anesthesia. 67, pp. 1152-1161, 2020.
  • H. Zhu, S. Salcudean, R. Rohling. Hand-eye coordination based implicit re-calibration method for gaze tracking on ultrasound machines: a statistical approach. International Journal of Computer Assisted Radiology and Surgery. 15, pp. 837-845, 2020.
  • Z. Liao, H. Girgis, A. Abdi, H. Vaseli, J. Hetherington, R. Rohling, K. Gin, T. Tsang, P. Abolmaesumi. On modelling label uncertainty in deep neural networks: automatic estimation of intra-observer variability in 2D echocardiography quality assessment. IEEE Trans. Medical Imaging. 39(6), pp. 1868-1883, 2020.
  • D. Behnami, C. Luong, H. Vaseli, A Abdi, H. Girgis, D. Hawley, R. Rohling, K. Gin, P. Abolmaesumi, T. Tsang. Automatic cine-based detection in patients at high risk of heart failure with reduced ejection fraction in echocardiograms. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. Published on-line 7 October 2019, pp 1-7, 2019.
  • H. Zhu, S. Salcudean, R. Rohling. The Neyman Pearson detection of microsaccades with maximum likelihood estimation of parameters. Journal of Vision 19(13):17, pp 1-17, 2019.
  • M. Pesteie, P. Abolmaesumi, R. Rohling. Adaptive augmentation of medical data using independently conditional variational auto-encoders. IEEE Trans. Medical Imaging. 38(12), pp. 2807-2820, 2019.
  • B. Zhuang, R. Rohling, P. Abolmaesumi. Region of interest based closed-loop beamforming for spinal ultrasound imaging. IEEE UFFC. 66(8), pp. 1266-1280, 2019.
  • M. Ai, S. Salcudean, R. Rohling, P. Abolmaesumi, S. Tang. Photoacoustic tomography for imaging prostate: a transurethral illumination probe design and application. Biomedical Optics Express. 10(5), pp. 2588-2605, 2019.
  • H. Zhu, S. Salcudean, R. Rohling. A novel gaze-supported multimodal human computer interaction for ultrasound machines. International Journal of Computer Assisted Radiology and Surgery. 14(7), pp. 1107-1115, 2019. Also appears as [C188] below.
  • R. Hu, R. Singla, F. Deeba, R. Rohling. Acoustic shadow detection from scanline statistics of B-mode or radiofrequency ultrasound images. Ultrasound in Medicine and Biology. 45(8), pp. 2248-2257, 2019.
  • M. Jafari, H. Girgis, N. van Woudenberg, Z. Liao, R. Rohling, K. Gin, P. Abolmaesumi, T. Tsang. Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training. International Journal of Computer Assisted Radiology and Surgery. 14(6), pp. 1027-1037, 2019. AI Translation in Medicine Award at conference.
  • A. Sedghi, M. Pesteie, S. Azizi, G. Javadi, P. Yan, J Tae Kwak, S. Xu, B. Turkbey, P. Choyke, P. Pinto, B. Wood, R. Rohling, P. Abolmaesumi, P. Mousavi. Deep neural maps for unsupervised visualization of high grade cancer in prostate biopsies. International Journal of Computer Assisted Radiology and Surgery. 14(24), pp. 1-8, 2019.
  • H. Vaseli, Z. Liao, A.H. Abdi, H. Girgis, D. Behnami, C. Luong, F.T. Dezaki, N. Dhungel, R. Rohling, K. Gin, and P. Abolmaesumi, “Designing lightweight deep learning models for echocardiography view classification,” In Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10951, p. 109510F). International Society for Optics and Photonics, 2019.
  • L.R. Porto, R. Tang, A. Sawka, V. Lessoway, E.M.A. Anas, D. Behnami, P. Abolmaesumi, and R. Rohling, “Comparison of Patient Position and Midline Lumbar Neuraxial Access Via Statistical Model Registration to Ultrasound,” Ultrasound in medicine & biology45(1), pp.255-263, 2019.
  • S. Honigmann, Y.C. Zhu, R. Singla, P. Abolmaesumi, A. Chau, and R. Rohling, “EpiGuide 2D: visibility assessment of a novel multi-channel out-of-plane needle guide for 2D point of care ultrasound,” In Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10951, p. 109510K). International Society for Optics and Photonics, 2019.
  • M.H. Jafari, H. Girgis, N. Van Woudenberg, Z. Liao, R. Rohling, K. Gin, P. Abolmaesumi, and T. Tsang, “Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training,” International journal of computer assisted radiology and surgery, pp.1-11, 2019.
  • F. Deeba, M. Ma, M. Pesteie, J. Terry, D. Pugash, J.A. Hutcheon, C. Mayer, S. Salcudean, and R. Rohling, “Attenuation Coefficient Estimation of Normal Placentas,” Ultrasound in medicine & biology, 2019.
  • M. Pesteie, V. Lessoway, P. Abolmaesumi, and R.N. Rohling, “Automatic localization of the needle target for ultrasound-guided epidural injections,” IEEE transactions on medical imaging37(1), pp.81-92, 2018.
  • I. Peterlík, H. Courtecuisse, R. Rohling, P. Abolmaesumi, C. Nguan, S. Cotin, and S. Salcudean, “Fast elastic registration of soft tissues under large deformations,” Medical image analysis45, pp.24-40, 2018.
  • M. Ai, T. Salcudean, R. Rohling, P. Abolmaesumi, and S. Tang, “Transurethral illumination probe design for deep photoacoustic imaging of prostate,” In Photons Plus Ultrasound: Imaging and Sensing 2018 (Vol. 10494, p. 104940C). International Society for Optics and Photonics, 2018.
  • C.D. Gerardo, E. Cretu, and R. Rohling, “Fabrication and testing of polymer-based capacitive micromachined ultrasound transducers for medical imaging,”Nature Microsystems & Nanoengineering4(1), p.19, 2018.
  • B. Zhuang, R. Rohling, and P. Abolmaesumi, “Accumulated angle factor-based beamforming to improve the visualization of spinal structures in ultrasound images,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control65(2), pp.210-222, 2018.
  • M. Pesteie, P. Abolmaesumi, and R. Rohling, “Deep neural maps,” arXiv preprint arXiv:1810.07291, 2018.
  • G. Samei, O. Goksel, J. Lobo, O. Mohareri, P. Black, R. Rohling, and S. Salcudean, “Real-time FEM-based registration of 3-D to 2.5-D transrectal ultrasound images,” IEEE transactions on medical imaging37(8), pp.1877-1886, 2018.
  • P. Edgcumbe, R. Singla, P. Pratt, C. Schneider, C. Nguan, and R. Rohling, “Follow the light: projector-based augmented reality intracorporeal system for laparoscopic surgery,” Journal of Medical Imaging5(2), p.021216, 2018.
  • M.H. Jafari, H. Girgis, Z. Liao, D. Behnami, A. Abdi, H. Vaseli, C. Luong, R. Rohling, K. Gin, T. Tsang, and P. Abolmaesumi, “A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data,” In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 29-37). Springer, Cham, 2018.

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