Lilei's research
Lei Li
Biomedical Science
Biomedial Engineering, Oxford University
Overview
Lei Li
2024-02-07 04:18:50

Biography

Lei Li is a Postdoctoral Research Assistant at the Institute of Biomedical Engineering, University of Oxford. She obtained her PhD degree from the School of Biomedical Engineering, Shanghai Jiao Tong University in 2021. During her PhD, she was a visiting PhD student at Fudan University and King’s College London, respectively. She obtained the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship. Her research interest is at the interface between machine learning and medical imaging, including developing novel computational methods for medical image analysis as well as translating the methods to clinical research and healthcare. She has already published more than 30 papers in peer-reviewed journals and interregnal conferences, including MedIA, IEEE TMI, and MICCAI. Some of these works have been selected as the most popular cited paper in MedIA. She is now one of the Board Members of Women in MICCAI (WiM) and an Editorial Board Member of the Journal of Medical Artificial Intelligence. She has been co-organizer of four MICCAI challenge events, including LAScarQS 2022, MyoPS 2020, MS-CMRSeg 2019, and MM-WHS 2017. She is a reviewer for many journals and conferences, including MedIA, IEEE TMI, IEEE TBME, Neurocomputing, IPMI, ISBI, IPMI, MIDL, and MICCAI.

Research/Education Experience

  • 08/2021-now   Postdoctoral Research Assistant, Institute of Biomedical Engineering, University of Oxford, Oxford, UK (Related Academics: Professor Vicente Grau)
  • 09/2016-06/2021   PhD, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China (Supervisor：Professor Xiahai Zhuang)
  • 12/2019-03/2021   Visiting PhD, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK (Supervisor：Professor Julia A Schnabel)
  • 12/2017-12/2019   Visiting PhD, School of Data Science, Fudan University, Shanghai, China (Supervisor：Professor Xiahai Zhuang)
  • 09/2012-06/2016   Bachelor, Dept. Medical Information Engineering, Sichuan University, Chengdu, China (Related Academics: Gang Yang)

Selected Publications

  • Lei Li, Veronika A Zimmer, Julia A Schnabel, Xiahai Zhuang: Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review, Medical Image Analysis*, 102360, 2022. Link

  • Lei Li, Veronika A Zimmer, Julia A Schnabel, Xiahai Zhuang*: 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. Link, Code

  • Lei Li, Fuping Wu, Guang Yang, Lingchao Xu, Tom Wong, Raad Mohiaddin, David Firmin, Jenny Keegan, Xiahai Zhuang*: Atrial Scar Quantification via Multi-Scale CNN in the Graph-Cuts Framework. Medical Image Analysis, vol. 60, 101595, 2020. Link, Code

  • Lei Li, Veronika A Zimmer, Julia A Schnabel, Xiahai Zhuang: AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs, MICCAI*, 557–566, 2021. Link, Video

  • Lei Li, Xin Weng, Julia A Schnabel, Xiahai Zhuang*: Joint Left Atrial Segmentation and Scar Quantification based on a DNN with Spatial Encoding and Shape Attention, MICCAI, 118-127, 2020. Link, Video, Code

Talks/Posters

  • 14/12/2022   Invited talk "Artificial Intelligence in Cardiac Image Computing and Modeling", which is hosted by Prof. Evangelos B. Mazomenos at University College London.
  • 18/09/2022   Oral presenter in STACOM 2022 for the paper "Deep Computational Model for the Inference of Ventricular Activation Properties". Video
  • 17/06/2022   Invited posters at SmartHeart Conference 2022.
  • 12/04/2022   Invited talk "Left Atrial LGE MRI Computing for Atrial Fibrillation" at MICS (in Chinese). PPT; Video
  • 27/09/2021   Oral presenter in M&Ms-2 for the paper "Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition". Video

Academic Services

News

  • [16 Nov, 2022] Our paper "MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images" has been accepted by MedIA. Bravo! Congrutulations to Junyi Qiu. Thanks so much to the editor and the reviewers!
  • [12 Oct, 2022] I was invited as a Editorial Board Member of the Journal of Medical Artificial Intelligence. Thanks so much for the invitation of Prof. Ming Xia.
  • [17 Aug, 2022] Our paper "Deep Computational Model for the Inference of Ventricular Activation Properties" has been accepted by STACOM 2022 and was selected as a oral presentation. Thanks so much to the reviewers!
  • [01 July, 2022] Our paper "Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge" has been accepted by MedIA! Thanks so much to the editor and the reviewers!
  • [17 June, 2022] I was invited present my two posters in SmartHeart Conference 2022. Thanks so much for the invitation of Prof. Julia A Schnabel.
  • [06 June, 2022] I was invited to join in Women in MICCAI (WiM) as a Board Member. Thanks so much for the invitation of Prof. Xiaoxiao Li (WiM President).
  • [02 June, 2022] Our paper "Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation" has been accepted by MICCAI 2022. Bravo! Congrutulations to Zheyao Gao. Thanks so much to the ACs and the reviewers!
  • [04 April, 2022] I was invited to give a talk on MICS China. Thanks for the inviatation of Prof. Liansheng Wang and the organization of Prof. Dan Long.
  • [01 Feb, 2022] Our paper "Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion" has been accepted by JBHI! Bravo! Congrutulations to Wangbin Ding.
  • [10 Jan, 2022] Our paper "Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review" has been accepted by MedIA! Thanks so much to Prof. Xiahai Zhuang and Prof. Julia A. Schnabel, the editor and the reviewers!
  • [10 Jan, 2022] Our paper "AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images" has been accepted by MedIA! Bravo! Congrutulations to Kaini Wang and Prof. Xin Yang.
  • [08 Jan, 2022] I got the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship! Thanks so much to Prof. Xiahai Zhuang, Prof. Julia A. Schnabel, Prof. Guoyan Zheng, and Prof. Vicent Grau, who all gave me much support on this application!
  • [08 Nov, 2021] Our paper "AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information" has been accepted by MedIA! Thanks so much to Prof. Xiahai Zhuang and Prof. Julia A. Schnabel, the editor and the reviewers!
  • [16 Aug, 2021] I have jointed the research team of Prof. Vicente Grau.
  • [06 Aug, 2021] I have reconstructed my personal website!
  • [12 June, 2021] Our paper "AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs" has been accepted by MICCAI 2021.
  • [26 May, 2021] I have passed my PhD viva! My PhD thesis is "Left Atrial Scar Segmentation and Quantification from Late Gadolinium Enhanced Magnetic Resonance Images". Thanks to my super nice supervisor Prof. Xiahai Zhuang and others who helped me during my PhD.
  • [23 June, 2020] Our paper "Joint Left Atrial Segmentation and Scar Quantification based on a DNN with Spatial Encoding and Shape Attention" has been accepted by MICCAI 2020.
  • [15 Dec, 2019] Happy to be the runnerups of FBDC best poster awards togther with Xin Weng.
  • [26 Oct, 2019] Our paper "Atrial Scar Quantification via Multi-Scale CNN in the Graph-Cuts Framework" has been accepted by MedIA! Thanks so much to Prof. Xiahai Zhuang, Dr. Guang Yang, the editor and the reviewers!
  • [22 July, 2019] Our paper "Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge" has been accepted by MedIA! Thanks so much to Prof. Xiahai Zhuang, Dr. Guang Yang, the editor and the reviewers!
Deep Computational Model for the Inference of Ventricular Activation Properties
STACOM, 369-380, 2023
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 inference of ventricular activation properties, i.e., conduction velocities and root nodes.
Keywords: Digital twin; Deep computational models; Ventricular activation properties; ECG simulation
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
Professor Xiahai Zhuang Professor Julia A Schnabel Professor Vicente Grau Fudan ZMIC Lab Oxford BioMedIA
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