Bo Li, MSc.

Bo Li

Office: Na 25-24
Phone: +31-10-7044131
Email: b.li@erasmusmc.nl

Curriculum Vitae

Bo Li was born in 1992 in Hohhot, China. She received her bachelor degree in Biomedical Engineering from the Northeastern University, China in 2013, where she studied medical informatics on the project of Survey-based risk assessment of stroke. In 2015, she received her MSc degree in Biomedical Engineering from the same university. She was jointly supervised by Prof. Jiren Liu and Prof. Bart M. ter Haar Romeny. Her master thesis focused on the computer-aided-diagnosis of neovascularization (diabetic retinopathy) on RetinaCheck project. Being highly motivated in this area, Bo continued her PhD study since 2015, where she researched Typical health care systems in worldwide and the inspiration for the reform of Chinese system, and natural language processing in Neusoft Corporation. During which, she accumulated her knowledge on the analysis of unstructured text data and data mining, and prepares for integrating imaging biomarkers and patient records within the health care system.

From 2017, Bo works as a visiting PhD student in BIGR group on the project of Quantitative analysis of white matter microstructure under the supervision of Dr. Esther Bron and Prof. Wiro Niessen. Her main interests are machine learning and neuro image analysis. She focuses on developing deep-learning based method for white matter tract segmentation, as well as improving the segmentation consistency and efficiency to support population-based imaging studies.

Research lines

Neuro Image Analysis

Publications

Conference Articles

  1. Li, B., de Groot, M., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & Bron, E. E. (2018). Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset. In International Workshop on Machine Learning in Medical Imaging (pp. 205–213). Springer.
  2. Li, B., de Groot, M., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & Bron, E. E. (2018). White Matter Tract Segmentation: Method development, application and assessment of generalizability. In EuSoMII Annual Meeting Advances in Medical Imaging with Informatics and Artificial Intelligence.