Imperial College London
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DrWenjiaBai

Faculty of Medicine, Department of Brain Sciences

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8291w.bai Website

 
 
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Location

 

Room 212, Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Basaran:2022:10.1007/978-3-031-16980-9_1,
author = {Basaran, BD and Qiao, M and Matthews, P and Bai, W},
doi = {10.1007/978-3-031-16980-9_1},
pages = {1--11},
publisher = {Springer},
title = {Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images},
url = {http://dx.doi.org/10.1007/978-3-031-16980-9_1},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis.
AU  - Basaran,BD
AU  - Qiao,M
AU  - Matthews,P
AU  - Bai,W
DO  - 10.1007/978-3-031-16980-9_1
EP  - 11
PB  - Springer
PY  - 2022///
SN  - 0302-9743
SP  - 1
TI  - Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images
UR  - http://dx.doi.org/10.1007/978-3-031-16980-9_1
UR  - http://hdl.handle.net/10044/1/99084
ER  -
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