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Lung image segmentation by generative adversarial networks [article]

Jiaxin Cai, Hongfeng Zhu
2019 arXiv   pre-print
Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks.  ...  The generative adversarial networks was employed to translate the original lung image to the segmented image.  ...  This paper proposed a lung segmentation method using Pix2Pix. The Pix2Pix was employed to translate the original lung image to the segmented image.  ... 
arXiv:1907.13033v1 fatcat:cb5el2ws3ra2pci6y2dv6urwgm

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays [article]

Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing
2017 arXiv   pre-print
In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images.  ...  During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network.  ...  Adversarial Training for Semantic Segmentation Adversarial training was first proposed in Generative Adversarial Network (GAN) [9] in the context of genera-tive modeling 1 .  ... 
arXiv:1703.08770v2 fatcat:rhsinssqufdplpgin3no4tgsiu

System segmentation of Lungs in images chest x-ray using the generative adversarial network

Omar El Mansouri, Yousef El Mourabit, Youssef El Habouz, S. Krit
2022 ITM Web of Conferences  
network to perform lung chest x-ray image segmentation.  ...  One of the most common medical imaging methods is a chest x-ray, as it contributes to the early detection of lung cancer compared to other methods. this work presents the use of a generative adversarial  ...  In this work, we propose using generative adversarial networks for segmentation.  ... 
doi:10.1051/itmconf/20224301020 fatcat:dihm4373hrcxdlsdszy4i2w6wm

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays [chapter]

Wei Dai, Nanqing Dong, Zeya Wang, Xiaodan Liang, Hao Zhang, Eric P. Xing
2018 Lecture Notes in Computer Science  
In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images.  ...  Through an adversarial process, the critic network guides the segmentation network to achieve more realistic segmentation that mimics the ground truth.  ...  Adversarial training was first proposed in Generative Adversarial Network (GAN) [7] in the context of generative modeling.  ... 
doi:10.1007/978-3-030-00889-5_30 fatcat:fkhkm2pf6zf7hgwevudo6b2vum

LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network [article]

Jiaxing Tan, Longlong Jing, Yumei Huo, Yingli Tian, Oguz Akin
2019 arXiv   pre-print
Pursuing an automatic segmentation method with fewer steps, in this paper, we propose a novel deep learning Generative Adversarial Network (GAN) based lung segmentation schema, which we denote as LGAN.  ...  Our proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images and is evaluated on a dataset containing 220 individual CT scans with two metrics: segmentation  ...  In this paper, to solve the medical image segmentation problem, especially the problem of lung segmentation in CT scan images, we propose LGAN (Generative Adversarial Network based Lung Segmentation) schema  ... 
arXiv:1901.03473v1 fatcat:3truhntzu5fgtfdwd3gsadbjsa

Segmentation of Lungs in Chest X-ray Image using Generative Adversarial Networks

Faizan Munawar, Shoaib Azmat, Talha Iqbal, Christer Gronlund, Hazrat Ali
2020 IEEE Access  
This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR.  ...  GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR.  ...  In this study, lung image segmentation is performed from chest x-ray images using generative adversarial networks.  ... 
doi:10.1109/access.2020.3017915 fatcat:ykfyjh3ii5grnh5cgvpnbajoo4

Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images [article]

Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne
2021 arXiv   pre-print
segmentations in ways that conflict with class adjacency priors learned by the target network.  ...  We showed that, by adding almost imperceptible noise to the image, we can reliably force state-of-the-art neural networks to segment the heart as a heart symbol instead of its real anatomical shape.  ...  In our study, this is exemplified by the network trying to preserve adjacency of segmentations of clavicles and lungs (from the same side) despite the adversarial attack target- Table 3 .  ... 
arXiv:2104.00139v2 fatcat:xvxu77dtc5f5bhiwrispnfbxeu

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

Xiang He, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, Yizhou Yu
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Experiments on both lung and skin lesion segmentation datasets have demonstrated that NLCEN outperforms any other state-of-the-art biomedical image segmentation methods against adversarial attacks.  ...  Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention.  ...  are the first to explore adversarial attacks on image segmentation and detection on large datasets and propose the density adversary generation to generate effective adversarial samples by considering  ... 
doi:10.1609/aaai.v33i01.33018417 fatcat:prvvbl542rbwrbkberb5nma3gy

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks [article]

Xiang He, Sibei Yang, Guanbin Li?, Haofeng Li, Huiyou Chang, Yizhou Yu
2019 arXiv   pre-print
Experiments on both lung and skin lesion segmentation datasets have demonstrated that NLCEN outperforms any other state-of-the-art biomedical image segmentation methods against adversarial attacks.  ...  Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention.  ...  are the first to explore adversarial attacks on image segmentation and detection on large datasets and propose the density adversary generation to generate effective adversarial samples by considering  ... 
arXiv:1904.12181v1 fatcat:3zxbx3zmkffnflboj64obkaknu

Domain Adaptation based COVID-19 CT Lung Infections Segmentation Network [article]

Han Chen and Yifan Jiang and Hanseok Ko
2020 arXiv   pre-print
This makes the embedding distribution learned by segmentation network from real data and synthetic data closer, thus greatly improving the representation ability of the segmentation network.  ...  To overcome the domain mismatch, we introduce conditional GAN for adversarial training. We update the segmentation network with the cross-domain adversarial loss.  ...  We also demonstrated the effectiveness of our network in lung segmentation. Our proposed method has great potential for use in diagnosing COVID-19 by quantifying the infected areas of the lung.  ... 
arXiv:2011.11242v1 fatcat:nyu7cz2ccjd3znlz2ftbejapwi

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation [article]

Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng
2018 arXiv   pre-print
In this paper, we present a novel unsupervised domain adaptation approach for segmentation tasks by designing semantic-aware generative adversarial networks (GANs).  ...  We validated our method on two different chest X-ray public datasets for left/right lung segmentation.  ...  In this work, we propose a semantic-aware generative adversarial networks for unsupervised domain adaptation (named SeUDA) of medical image segmentation.  ... 
arXiv:1806.00600v2 fatcat:fg6qdglnh5d4bgshjdkt34rzc4

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation [chapter]

Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng
2018 Lecture Notes in Computer Science  
In this paper, we present a novel unsupervised domain adaptation approach for segmentation tasks by designing semantic-aware generative adversarial networks (GANs).  ...  We validated our method on two different chest X-ray public datasets for left/right lung segmentation.  ...  In this work, we propose a semantic-aware generative adversarial networks for unsupervised domain adaptation (named SeUDA) of medical image segmentation.  ... 
doi:10.1007/978-3-030-00919-9_17 fatcat:cwhhwzhh45bu3e7rsjipptkumy

Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation [article]

Gusztáv Gaál, Balázs Maga, András Lukács
2020 arXiv   pre-print
Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model.  ...  Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline.  ...  Acknowledgments and Disclosure of Funding The project was partially supported by the AI4EU project, funded by EU H2020 programme (contract no. 825619).  ... 
arXiv:2003.10304v1 fatcat:fkla4k3z2bcufpgyksnld353oa

Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information [article]

Dwarikanath Mahapatra, Zongyuan Ge
2019 arXiv   pre-print
This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning.  ...  Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types  ...  We leverage segmentation information and transfer learning with generative adversarial networks.  ... 
arXiv:1903.10139v2 fatcat:zqv677ptyrbivivtiydkj5vpfe

A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images

Hai-yan Yao, Wang-gen Wan, Xiang Li
2022 EURASIP Journal on Advances in Signal Processing  
To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically.  ...  Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images.  ...  GANs are learned by playing a minimax optimization game between a generator network G and a discriminator network D.  ... 
doi:10.1186/s13634-022-00842-x pmid:35194421 pmcid:PMC8830991 fatcat:ch3ryiu5i5cnrfakca4vmnxpwu
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