Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network

Hu, Shengye; Shen, Yanyan; Wang, Shuqiang; Lei, Baiying

doi:10.1007/978-3-030-59713-9_67

Cited by 54 publications

(14 citation statements)

References 17 publications

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“…As the optimization theory [30] and deep learning technology developed in recent years, the medical images computing technology has made great success. For example, automatic diagnosis [32, 34-38, 50, 52], disease prediction [19,20,44], medical image synthesis [8][9][10][11]51].…”

Section: Related Work 21 Medical Image Computingmentioning

confidence: 99%

Brain MR Images Super-Resolution with the Consistent Features

You

Shen

et al. 2022

2022 14th International Conference on Machine Learning and Computing (ICMLC)

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Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and highresolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively. CCS CONCEPTS• Computing methodologies → Machine Learning.

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Section: Related Work 21 Medical Image Computingmentioning

confidence: 99%

Brain MR Images Super-Resolution with the Consistent Features

You

Shen

et al. 2022

2022 14th International Conference on Machine Learning and Computing (ICMLC)

Self Cite

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“…Recently, deep learning technology has been popularized in medical image processing, and has been applied in maturity recognition [5], [6], disease analysis [7], cross-modal data supplement [8] and other fields. Many deep learning reconstruction models, such as Generative adversarial networks (GANs) and auto encoders (AEs), are widely used in reconstructing 2D images.…”

Section: Related Workmentioning

confidence: 99%

3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image

Hu¹,

Lei²,

Liu

et al. 2021

Preprint

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3D shape reconstruction is essential in the navigation of minimally-invasive and auto robot-guided surgeries whose operating environments are indirect and narrow, and there have been some works that focused on reconstructing the 3D shape of the surgical organ through limited 2D information available. However, the lack and incompleteness of such information caused by intraoperative emergencies (such as bleeding) and risk control conditions have not been considered. In this paper, a novel hierarchical shape-perception network (HSPN) is proposed to reconstruct the 3D point clouds (PCs) of specific brains from one single incomplete image with low latency. A tree-structured predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality. Meanwhile, attention gate blocks (AGBs) are designed to efficiently aggregate geometric local features of incomplete PCs transmitted by hierarchical attention pipelines and internal features of reconstructing point clouds. With the proposed HSPN, 3D shape perception and completion can be achieved spontaneously. Comprehensive results measured by Chamfer distance and PC-to-PC error demonstrate that the performance of the proposed HSPN outperforms other competitive methods in terms of qualitative displays, quantitative experiment, and classification evaluation.

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“…However, to the best of our knowledge, no effort has been devoted to the development of point cloud reconstruction of brains. Since Deep learning technology has been popularized in the medical prediction [16,17,18], and has been applied in many fields such as maturity recognition [19,20], disease analysis [21,22,23,24,25], data generation [26,27], there are many works that combine deep learning with 3D data for accurate reconstruction [28,29,30]. Generative adversarial network, as well as many of its variants [31,32,33], is a widely used generative model and is known for its good generation quality.…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction

Lei

Shen

et al. 2021

Preprint

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Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery. However, compared to the substantial image data, it is almost impossible to obtain the intraoperative 3D shape information by using physical methods such as sensor scanning, especially in minimally invasive surgery and robot-guided surgery. In this paper, a general generative adversarial network (GAN) architecture based on graph convolutional networks is proposed to reconstruct the 3D point clouds (PCs) of brains by using one single 2D image, thus relieving the limitation of acquiring 3D shape data during surgery. Specifically, a tree-structured generative mechanism is constructed to use the latent vector effectively and transfer features between hidden layers accurately. With the proposed generative model, a spontaneous image-to-PC conversion is finished in real-time. Competitive qualitative and quantitative experimental results have been achieved on our model. In multiple evaluation methods, the proposed model outperforms another common point cloud generative model PointOutNet.

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Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network

Cited by 54 publications

References 17 publications

Brain MR Images Super-Resolution with the Consistent Features

Brain MR Images Super-Resolution with the Consistent Features

3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction

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