Transformer based Generative Adversarial Network for Liver Segmentation

TL;DR

This paper introduces a hybrid Transformer-GAN model for liver segmentation in medical images, leveraging attention mechanisms for improved accuracy and reliability over existing methods.

Contribution

The study presents a novel hybrid architecture combining Transformers with GANs for liver segmentation, enhancing global feature modeling and segmentation credibility assessment.

Findings

Achieved a dice coefficient of 0.9433

Outperformed other Transformer-based methods

Provided more reliable segmentation results

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.