Transformer Based Generative Adversarial Network for Liver Segmentation

Ugur Demir, Zheyuan Zhang, Bin Wang, Matthew Antalek, Elif Keles, Debesh Jha, Amir Borhani, Daniela Ladner, Ulas Bagci*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolution neural networks (CNNs) have became the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation algorithm using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling. This mechanism provides better segmentation performance compared with traditional methods. Furthermore, we encode this generator into the GAN based architecture so that the discriminator network in the GAN can classify the credibility of the generated segmentation masks compared with the real masks coming from human (expert) annotations. This allows us to extract the high dimensional topology information in the mask for biomedical image segmentation and provide more reliable segmentation results. Our model achieved a high dice coefficient of 0.9433, recall of 0.9515, and precision of 0.9376 and outperformed other Transformer based approaches.

Original languageEnglish (US)
Title of host publicationImage Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers
EditorsPier Luigi Mazzeo, Cosimo Distante, Emanuele Frontoni, Stan Sclaroff
PublisherSpringer Science and Business Media Deutschland GmbH
Pages340-347
Number of pages8
ISBN (Print)9783031133237
DOIs
StatePublished - 2022
Event21st International Conference on Image Analysis and Processing , ICIAP 2022 - Lecce, Italy
Duration: May 23 2022May 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13374 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Image Analysis and Processing , ICIAP 2022
Country/TerritoryItaly
CityLecce
Period5/23/225/27/22

Keywords

  • Generative adversarial network
  • Liver segmentation
  • Transformer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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