Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images
File(s)
Accepted version
Author(s)
Basaran, Berke Doga
Qiao, Mengyun
Matthews, Paul
Bai, Wenjia
Type
Conference Paper
Abstract
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.
Date Issued
2022-09-21
Date Acceptance
2022-07-23
Citation
Lecture Notes in Computer Science, 2022, 13570, pp.1-11
ISSN
0302-9743
Publisher
Springer
Start Page
1
End Page
11
Journal / Book Title
Lecture Notes in Computer Science
Volume
13570
Copyright Statement
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-031-16980-9_1
Source
SASHIMI: Simulation and Synthesis in Medical Imaging
Publication Status
Published
Start Date
2022-09-18
Coverage Spatial
Singapore