@inproceedings{3a44c556fc8d481d8604cf4b954c802b,
title = "Transformer Based Generative Adversarial Network for Liver Segmentation",
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.",
keywords = "Generative adversarial network, Liver segmentation, Transformer",
author = "Ugur Demir and Zheyuan Zhang and Bin Wang and Matthew Antalek and Elif Keles and Debesh Jha and Amir Borhani and Daniela Ladner and Ulas Bagci",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Image Analysis and Processing , ICIAP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1007/978-3-031-13324-4_29",
language = "English (US)",
isbn = "9783031133237",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "340--347",
editor = "Mazzeo, {Pier Luigi} and Cosimo Distante and Emanuele Frontoni and Stan Sclaroff",
booktitle = "Image Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers",
address = "Germany",
}