Transformer based Generative Adversarial Network for Liver Segmentation

• URL: http://arxiv.org/abs/2205.10663v1
• Date: Sat, 21 May 2022 19:55:43 GMT
• Title: Transformer based Generative Adversarial Network for Liver Segmentation
• Authors: Ugur Demir, Zheyuan Zhang, Bin Wang, Matthew Antalek, Elif Keles, Debesh Jha, Amir Borhani, Daniela Ladner and Ulas Bagci
• Abstract summary: We propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. 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.
• Score: 4.317557160310758
• License: http://creativecommons.org/licenses/by/4.0/
• 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 convolutional neural networks (CNNs) have become 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 approach 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.

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