Multiple GANs guided by self-attention mechanism for automatic cardiac image segmentation

Chang Yuwen; Lei Jiang; Hengfei Cui

doi:10.1117/12.2623153

16 February 2022 Multiple GANs guided by self-attention mechanism for automatic cardiac image segmentation

Chang Yuwen, Lei Jiang, Hengfei Cui

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831M (2022) https://doi.org/10.1117/12.2623153
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

In this paper, we propose a cross-modal unsupervised domain adaptive cardiac substructure automatic segmentation method to address the problem of a sharp decline in performance of a neural network trained in a specific imaging domain to migrate to another target domain that lacks annotations. The proposed multiple generative adversarial networks guided by self-attention framework is called GANSA for short. GANSA eliminates the differences in image appearance and features between different domains (MRI and CT) through GANs, and uses cyclic consistency loss to ensure that the features of the image itself remain unchanged. We use the self-attention mechanism to control GANs, which ensures the details of the generated image, and pays attention to the connection of long-distance information of the image, in order to complete the cross-modal cardiac segmentation task. The proposed framework is trained and tested on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset. Experimental results show that our method outperforms the state-of-the-art method.

Citation Download Citation

Chang Yuwen, Lei Jiang, and Hengfei Cui "Multiple GANs guided by self-attention mechanism for automatic cardiac image segmentation", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831M (16 February 2022); https://doi.org/10.1117/12.2623153

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KEYWORDS

Image segmentation

Magnetic resonance imaging

Gallium nitride

Computed tomography

Image processing

Computer programming

Image enhancement

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