MLP-GAN for Brain Vessel Image Segmentation

• URL: http://arxiv.org/abs/2207.08265v1
• Date: Sun, 17 Jul 2022 19:24:38 GMT
• Title: MLP-GAN for Brain Vessel Image Segmentation
• Authors: Bin Xie, Hao Tang, Bin Duan, Dawen Cai, Yan Yan
• Abstract summary: Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and to learn a conditional Generative Adversarial Network (cGAN) to learn a transformation between two distributions. We present a novel multi-view approach, which perform a 3D volumetric brain vessel image into three different 2D images (i.e., sagittal, coronal, axial) and then feed them into three different 2D cGANs. Our model obtains the ability to capture cross-patch information
• Score: 19.807219907693145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and perform a conditional Generative Adversarial Network (cGAN) to learn a transformation between two distributions. In this paper, we present a novel multi-view approach, MLP-GAN, which splits a 3D volumetric brain vessel image into three different dimensional 2D images (i.e., sagittal, coronal, axial) and then feed them into three different 2D cGANs. The proposed MLP-GAN not only alleviates the memory issue which exists in the original 3D neural networks but also retains 3D spatial information. Specifically, we utilize U-Net as the backbone for our generator and redesign the pattern of skip connection integrated with the MLP-Mixer which has attracted lots of attention recently. Our model obtains the ability to capture cross-patch information to learn global information with the MLP-Mixer. Extensive experiments are performed on the public brain vessel dataset that show our MLP-GAN outperforms other state-of-the-art methods. We release our code at https://github.com/bxie9/MLP-GAN

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