Hua Ma

Office: Na 26-23k

Phone: +31-10-7038907

Email: h.ma@erasmusmc.nl

Address: Dr. Molewaterplein 50, 3015 GE Rotterdam


Curriculum Vitae


Hua Ma obtained a Bsc degree in Biological Sciences (2009) at Sun Yat-Sen University in Guangzhou, China. In 2010, he moved to Switzerland to start his master study at ETH Zurich where he developed interests in image processing for (bio)medical images and machine learning. In 2013, he obtained a Msc degree in Biomedical Engineering. His master thesis topic was retrieval of optimal CT images for atlas-based segmentation for the treatment planning in radiotherapy, jointly supervised by Prof. Orcun Goksel from the Computer Vision Laboratory at ETH Zurich and Dr. Benjamin Haas from Varian Medical Systems Imaging Laboratory GmbH (Baden, Switzerland)

Since 2014, Hua has been a PhD student in Biomedical Imaging Group Rotterdam headed by Prof. Wiro Niessen. He has been involved in the IMAGIC project which aims at developing intelligent image guidance technologies for cardiac interventions, under supervision by Dr. Theo van Walsum. During the PhD time, he has been focusing on novel techniques in medical imaging, computer vision and machine learning, and applying them to develop innovative solutions for clinical problems in the workflows of cardiac interventions. His current research interests include technologies of computer vision, machine learning and deep learning for medical imaging problems.


Research lines


Image Guidance in Interventions


Projects


IMAGIC – Intelligent iMAge Guidance In Cardiac interventions


Publications

Journal Articles

  1. Ma, H., Hoogendoorn, A., Regar, E., Niessen, W. J., & van Walsum, T. (2017). Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions. Medical Image Analysis, 39, 145–161.
  2. Ma, H., Dibildox, G., Schultz, C., Regar, E., & van Walsum, T. (2015). PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions. International Journal of Computer Assisted Radiology and Surgery, 10(6), 695–705.

Conference Articles

  1. Hao, H., Ma, H., & van Walsum, T. (2018). Vessel layer separation in X-ray angiograms with fully convolutional network. In Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling (pp. 10576–10576 - 15). International Society for Optics and Photonics. https://doi.org/10.1117/12.2293561
  2. Ma, H., Ambrosini, P., & van Walsum, T. (2017). Fast prospective detection of contrast inflow in X-ray angiograms with convolutional neural network and recurrent neural network. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 453–461). Springer.
  3. Ma, H., Dibildox, G., Banerjee, J., Niessen, W., Schultz, C., Regar, E., & van Walsum, T. (2015). Layer separation for vessel enhancement in interventional X-ray angiograms using morphological filtering and robust PCA. In MICCAI 2015 Workshop on Augmented Environments for Computer-Assisted Interventions (pp. 104–113). Springer.

Abstracts

  1. Ma, H., Dibildox, G., Banerjee, J., Niessen, W., Schultz, C., Regar, E., & van Walsum, T. (2016). Layer separation for vessel enhancement in interventional X-ray angiograms using morphological closing and robust PCA. In The 28th Conference of the international Society for Medical Innovation and Technology (SMIT).
  2. Ma, H., Coradi, T., Székely, G., Haas, B., & Göksel, O. (2013). Supervised learning with global features for image retrieval in atlas-based segmentation of thoracic CT. In Computer assisted radiology and surgery: proceedings of the 27th international congress and exhibition, Heidelberg, Germany, June 26-29, 2013 (Vol. 8, pp. 302–303). Springer.