Lei's Homepage
Lei Li
Assistant Professor
Digital Heart Lab, NUS
Overview
Lei Li
2024-10-27 10:35:32

Biography

Lei Li is an Assistant Professor at the Department of Biomedical Engineering, National University of Singapore and PI of Digital Heart Lab (DHlab). Previously, she was a Lecturer at the School of Electronics & Computer Science, University of Southampton. She received her PhD degree from the School of Biomedical Engineering, Shanghai Jiao Tong University in 2021 and obtained the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship. Her research interest is AI for healthcare, specifically focusing on cardiac digital twins, medical imaging, and multi-modal AI. She is a Board Member of SIG-Cardiac and Women in MICCAI (WiM) and Area Chair of MICCAI 2024. She has been selected as the Rising Star of Women in Engineering by Asian Deans’ Forum 2023 and Featured Women in Science by RSIP Vision.

📢📢📢 I am on looking for self-motivated PhD students who are interested in AI for heathcare. For more details, please check our DHlab website ✨.

Research/Education Experience

  • 10/2024-now   Assistant Professor, Department of Biomedical Engineering, National University of Singapore, Singapore
  • 10/2024-now   Visiting Researcher, School of Electronics & Computer Science, University of Southampton, Southampton, UK
  • 01/2024-now   Visiting Researcher, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
  • 01/2024-10/2024   Lecturer, School of Electronics & Computer Science, University of Southampton, Southampton, UK
  • 08/2021-12/2023   Postdoctoral Research Assistant, Institute of Biomedical Engineering, University of Oxford, Oxford, UK (Supervisor: Prof. Vicente Grau)
  • 09/2016-06/2021   PhD, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China (Supervisor: Prof. Xiahai Zhuang)
  • 01/2020-03/2021   Visiting PhD, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK (Supervisor: Prof. Julia A Schnabel)
  • 12/2017-12/2019   Visiting PhD, School of Data Science, Fudan University, Shanghai, China (Supervisor: Prof. Xiahai Zhuang)
  • 09/2012-06/2016   Bachelor, Dept. Medical Information Engineering, Sichuan University, Chengdu, China (Supervisor: Prof. Gang Yang, Prof. Site Mo)

Academic Services

Honors & Awards

  • 2024: IEEE TMI Distinguished Reviewer - Gold Level (2023-2024)
  • 2024: 2nd Prize of Shanghai Science and Technology Award (Natural Science Award)
  • 2024: Featured Woman in Science at RSIP Vision
  • 2023: The Rising Star of Women in Engineering - Asian Deans’ Forum 2023
  • 2023: IEEE TMI Distinguished Reviewer - Gold Level (2022-2023)
  • 2022: IEEE TMI Distinguished Reviewer - Bronze Level (2020-2022)
  • 2022: SJTU 2021 Outstanding Doctoral Graduate Development Scholarship

Selected Invited Talks

  • 03/06/2024   Invited talk “AI Powered Multi-Modal Cardiac Data Analysis: Towards to Cardiac Digital Twins for Personalized Healthcare”, in Isaac Newton Institute Workshop - Fickle Heart: The intersection of UQ, AI and Digital Twins. (Cambridge, UK)
  • 19/05/2024   Invited talk “Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference”, in Heart Rhythm 2024. (Boston, USA)
  • 2024-04-23   AI powered cardiac digital twins: towards to personalized cardiac arrythmia treatment” at IBME.ai Healthcare Workshop. (Oxford, UK)
  • 14/03/2024   Invited talk “AI Powered Virtual Heart Modelling for Personalized Cardiac Arrythmia Treatment” at Applied Mathematics Seminar – AI and Machine Learning at University of Southampton. (Southampton, UK)
  • 23/02/2024   Invited talk “AI Powered Personalized Virtual Heart Modelling” at Biomedical Research Centre (BRC) Science Forum at University of Southampton. (Southampton, UK)
  • 29/01/2024   Invited talk “Artificial Intelligence in Cardiac Image Computing and Modeling” at Department of Biomedical Engineering, National University of Singapore. (Online)
  • 18/07/2023   Invited talk “Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference”, in Multi-scale & Multi-modality Digital Health Collaboration Workshop, supported by the Alan Turing Institute. (London, UK)
  • 10/06/2023   Invited talk “Artificial Intelligence in Cardiac Image Computing and Modeling”, which is hosted by Prof. Yi Dai at Nankai University. Talk info (Online)
  • 15/02/2023   Invited talk “Artificial Intelligence in Cardiac Image Computing and Modeling”, which is hosted by Prof. Evangelos B. Mazomenos at University College London. (London, UK)
  • 08/12/2022   Invited talk “Domain Generalization and Distributed Learning for Left Atrial Segmentation from Multi-Center LGE-MRI”, in Turing workshop on Human-AI Interaction in Bio-Medicine. (Oxford, UK)
  • 12/04/2022   Invited talk “Left Atrial LGE MRI Computing for Atrial Fibrillation” at MICS (in Chinese). PPT; Video (Online)

Teaching Experience

  • Undergraduate Courses
    • Biomedical Image Analysis
      • Demonstrator: ~3 hours per week; Weeks 6-8 of Trinity Term 2023; University of Oxford
      • Demonstrator: ~3 hours per week; Weeks 6-8 of Trinity Term 2022; University of Oxford
    • Information Engineering Systems
      • Demonstrator: ~12 hours per week including providing undergraduate with guidance, feedback, and marking; Weeks 7-8 of Hilary Term 2023; University of Oxford
  • Postgraduate Courses
    • Medical Imaging, Microscopy and Analysis
      • Demonstrator: ~5 hours per week; Weeks 5-6 of Hilary Term 2023; University of Oxford
      • Demonstrator: ~5 hours per week; Weeks 5-6 of Hilary Term 2022; University of Oxford

Selected Works

Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
IEEE Transactions on Medical Imaging, 2024
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning-based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inverse inference of infarcted area.
Keywords: Cardiac digital twins; Myocardial Infarction; Deep computational models; Inverse inference
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Medical Image Analysis, vol. 77, 102360, 2022
LGE MRI is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies."
Keywords: Atrial fibrillation; LGE MRI; Left atrium; Review
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
Medical Image Analysis, vol. 76, 102303, 2022
LA and atrial scar segmentation from LGE MRI is an important task in clinical practice. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation.
Keywords: Joint optimization; Atrial segmentation; Scar quantification; Spatial encoding; Shape attention
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
MICCAI, 557-566, 2021
Though deep learning-based methods can provide promising LA segmentation results, they often generalize poorly to unseen domains, such as data from different scanners and/or sites. In this work, we collect 140 LGE MRIs from different centers with different levels of image quality. To evaluate the domain generalization ability of models on the LA segmentation task, we employ four commonly used semantic segmentation networks for the LA segmentation from multi-center LGE MRIs. Besides, we investigate three domain generalization strategies, i.e., histogram matching, mutual information based disentangled representation, and random style transfer, where a simple histogram matching is proved to be most effective.
Keywords: Atrial fibrillation; Left atrium; Multi-center LGE MRIs; Domain generalization
Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition
STACOM, 259-267, 2021
Right ventricular (RV) segmentation from MRI is a crucial step for cardiac morphology and function analysis. However, current methods for the RV segmentation tend to suffer from performance degradation at the basal and apical slices of MRI. In this work, we propose an automatic RV segmentation framework, where the information from long-axis (LA) views is utilized to assist the segmentation of short-axis (SA) views via information transition. Specifically, we employed the transformed segmentation from LA views as a prior information, to extract the ROI from SA views for better segmentation. The information transition aims to remove the surrounding ambiguous regions in the SA views."
Keywords: RV segmentation; Multi-view MRI; Information transition
Atrial Scar Quantification via Multi-Scale CNN in the Graph-Cuts Framework
Medical Image Analysis, vol. 60, 101595, 2020
LGE MRI appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the LA using a multi-scale CNN.
Keywords: Atrial fibrillation; Left atrium; LGE MRI; Graph learning; Multi-scale CNN
Links
Digital Heart Lab NUS BME Fudan ZMIC Lab Oxford BioMedIA Southampton DHBE Group
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