Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder

Jiayu Huo, Vejay Vakharia, Chengyuan Wu, Ashwini Sharan, Andrew L. Ko, Sebastien Ourselin, Rachel Sparks*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)
13 Downloads (Pure)


Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical. In this paper, we propose a progressive brain lesion synthesis framework (PAVAE) to expand both the quantity and diversity of the training dataset. Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network. To better employ extrinsic information to provide additional supervision during network training, we design a condition embedding block (CEB) and a mask embedding block (MEB) to encode inherent conditions of masks to the feature space. Finally, a segmentation network is trained using raw and synthetic lesion images to evaluate the effectiveness of the proposed framework. Experimental results show that our method can achieve realistic synthetic results and boost the performance of down-stream segmentation tasks above traditional data augmentation techniques.
Original languageEnglish
Title of host publicationSimulation and Synthesis in Medical Imaging - 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsCan Zhao, David Svoboda, Jelmer M. Wolterink, Maria Escobar
PublisherSpringer, Cham
Number of pages11
ISBN (Electronic)978-3-031-16980-9
ISBN (Print)978-3-031-16979-3
Publication statusPublished - 18 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13570 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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