Interests
- Deep Learning
- Neuroimage analysis
- Longitudinal Image Analysis
- Diffusion-weighted MRI
- Neurodegenerative diseases
Education
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PhD (cum laude) in Biomedical Engineering, 2020
Northestern University/CN
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MSc in Biomedical Engineering, 2015
Northestern University/CN
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BSc in Biomedical Engineering, 2013
Northestern University/CN
Research lines
Biography
Bo was born in Inner Mongolia, China in 1992. She received her bachelor’s degree in Biomedical Engineering from Northeastern University in 2013, where she studied medical informatics on the risk assessment of stroke. In 2015, she received her MSc degree from the same university supervised by Prof. Jiren Liu, Prof. Yan Kang, and Prof. Bart M. ter Haar Romeny. Her master’s 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 in 2015, where she researched typical healthcare systems worldwide and natural language processing (NLP) with the aim of integrating digital patient reports with medical imaging analysis within healthcare systems. From 2017, Bo joined the BIGR group as a visiting PhD student on NeuroImage Analysis & Deep Learning under the supervision of Dr. Esther Bron and Prof. Wiro Niessen, and continued as a postdoc scientist since 2020 on the projects of Medical Delta Diagnostics 3.0: Dementia and Stroke, and Heart-Brain Connection.
Research
Since her BSc study in 2010, she has been focusing on medical applications and machine (deep) learning. Her mission is to translate information technology to medical research and to clinical practice, especially to diseases that widely affect people’s health and social development, such as diabetes, dementia, and stroke. By gaining knowledge from previous patients, we improve the diagnosis and treatment for future patients. Therefore, she aims to develop reliable image analysis and prediction methodology, develop knowledge integration system to fuse multi-center imaging data and clinical data, and identify and overcome challenges for clinical use.
Grant allocation
- Convergence Open Mind 2021, “Neurodegeneration beyond DTI”, Principal investigator
Teaching and Supervison
PhD students (1)
- Wenjie Kang, ‘Interpretable machine learning for diagnosis and prognosis in neurodegenerative disease’ at Erasmus MC (01/09/2022 – now), co-supervisor
Master students (3)
- Claudia Chinea Hammecher, MSc Biomedical Engineering, TU Delft (2022), primary supervisor
- Liselot Goris, MSc Biomedical Engineering, U Twente (2021), primary supervisor
- Savine Martens, MSc Biomedical Engineering, TU Delft (2020), primary supervisor
Professional societies
Reviewer for the following journals:
- IEEE Transactions on Medical Imaging
- Medical Image Analysis
- Artificial Intelligence In Medicine
Reviewer for international conferences:
- Medical Image Computing and Computer-Assisted Interventions (MICCAI)
Societal quality of own research:
- Interview: “collaboration ensures that I can do effective research” (Medical Delta), 2022
- Interview: “the best of MICCAI selection” (MICCAI Daily), and Computer Vision News, 2019
Publications
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Li, B., de Groot, M., …, Niessen, W.J., and Bron, E.E.: Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging, NeuroImage 2020
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Li, B., Niessen, W.J., Klein, S., …, Vernooij, M.W., and Bron, E.E.: Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration, NeuroImage 2021
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De Planque, C., Gaillard, L., Vrooman, H.A., Li, B., van Veelen, M., Mathijssen, and I., Dremmen, M.: “An investigation on the white matter properties of the frontal lobe in trigonocephaly patients vs control: A diffusion tensor imaging study”. Pediatric Neurology 2022
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Sudre, C.H., …, Li, B., …, de Bruijne, M.: “Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021”, Medical Image Analysis (Under review, MedIA)
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Li, B., de Groot, M., Vernooij, M.W., Ikram, M.A., Niessen, W.J. and Bron, E.E.: Reproducible white matter tract segmentation using 3D U-Net on a large-scale DTI dataset. In 10th International Workshop on Machine Learning in Medical Imaging, Granada, Spain, 16 September 2018. (Poster)
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Li, B., de Groot, M., Vernooij, M.W., Ikram, M.A., Niessen, W.J. and Bron, E.E.: White Matter Tract Segmentation: Method development, application, and assessment of generalizability. At EuSoMII Annual Meeting ‘Advances in Medical Imaging with Informatics and Artificial Intelligence’, Rotterdam, the Netherlands, 3 November 2018. (Oral presentation)
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Li, B., Niessen, W.J., …, Vernooij, M.W., and Bron, E.E.: A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes. In 22nd MICCAI, Shenzhen, China, 13 October 2019. (Oral presentation)
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Li, B., Niessen, W.J., Klein, S., de Groot, M., Ikram, M.A., Vernooij, M.W. and Bron, E.E.: Learning unbiased registration and joint segmentation: evaluation on longitudinal diffusion MRI. In SPIE Medical Imaging, virtual conference / San Diego, United States,15-19 February 2021. (Oral presentation)
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Liu, X., Li, B., Bron, E.E, Niessen, J., Wolvius, J., and Roshchupkin, G.: Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis. In 24th MICCAI, 27 September – 1 October 2021. (Poster)
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Li, B., Liu, X., Niessen, W.J., …, Roshchupkin, G., and Bron, E.E.: A high-resolution autoencoder for construction of interpretable brain MRI endophenotypes. The Organization of Human Brain Mapping (OHBM) Annual meeting, Glasgow, Scotland, 19-23 June 2022. (Oral presentation)
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Li, B., de Bresser, J., Niessen, W.J., van Osch, M., van der Flier, W.M., Biessels, G.J., Vernooij, M.W., and Bron, E.E.: Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site MRI. The Organization of Human Brain Mapping (OHBM) Annual meeting, Glasgow, Scotland, 19-23 June 2022. (Poster)
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Chinea Hammecher, C., van Garderen, K., Smits, M., …, Vos, F., Bron, E.E Vernooij, M.W., Bron, E.E.., and Li, B.: Deep learning-based group-wise registration for longitudinal MRI analysis in glioma. ISMRM, Toronto, Canada, 3-8 June 2023. (Digital Poster) (Oral presentation at ISMRM Benelux)