Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks

Mina Rezaei; Haojin Yang; Christoph Meinel

doi:10.1117/12.2512215

13 March 2019 Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks

Mina Rezaei, Haojin Yang, Christoph Meinel

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095027 (2019) https://doi.org/10.1117/12.2512215
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Inspired by the recent success of generative adversarial networks (GANs), we propose a multi-agent GANs, named 3DJoinGANs, for handling imbalanced training data for the task of semantic segmentation. Our proposed method comprises two conditional GANs with four agents: a couple segmentors and a couple discriminators. The proposed framework learns a joint distribution of magnetic resonance images (MRI) and computed tomography images (CT) from different brain diseases by enforcing a weight-sharing constraint. While the first segmentor is trained on 3D multi-model MRI to learn semantic segmentation of a brain tumor(s), the first discriminator classifies whether predicted output by segmentor is real or fake. On the other hand, the second segmentor takes 3D multi modal CT images to learn segmentation of brain stroke lesions, and the second discriminator classifies between a segmented output by segmentor and a ground truth data annotated by an expert. We investigate, the 3DJoinGANs is able to mitigate imbalanced data problems and improve segmentation results due to oversampling and training through a joint distribution of cross-domain images.The proposed architecture has shown promising performance on the ISLES-2018 benchmark for segmentation of 3D multi modal ischemic stroke lesions and semantic segmentation of 3D multi modal brain tumors from the BraTS-2018 challenge.

Citation Download Citation

Mina Rezaei, Haojin Yang, and Christoph Meinel "Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095027 (13 March 2019); https://doi.org/10.1117/12.2512215

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Tumors

3D image processing

Brain

Computed tomography

Magnetic resonance imaging

Medical imaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years