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Combining generative and discriminative models for semantic segmentation of CT scans via active learning

Juan Eugenio Iglesias, Ender Konukoglu, Albert Montillo, Zhuowen Tu, Antonio Criminisi
2011 Information processing in medical imaging : proceedings of the ... conference  
The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy.  ...  Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov  ...  Pathak and Dr. S. White for the data, and Dr. C. Rother and Dr. J. Wortman Vaughan for useful suggestion. J.E. Iglesias and Dr. Tu were partially supported by NSF career award IIS-0844566.  ... 
pmid:21761643 fatcat:3xobt3nfjrhx7n2kqqyx4srhta

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Learning based Registration 291 CFCM: segmentation via Coarse to Fine Context Memory 295 Deep Learning from Label Proportions for Emphysema Quantification 296 Semi-Automatic RECIST Labeling on CT Scans  ...  for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation [article]

Jonathan Rubin, S. Mazdak Abulnaga
2019 arXiv   pre-print
We detail the architectures of the generator and discriminator and describe the training process used to perform image-to-image translation from multi-modal CT perfusion maps to diffusion weighted MR outputs  ...  Segmentation networks trained using generated CT-to-MR inputs result in at least some improvement on all metrics used for evaluation, compared with networks that only use CT perfusion input.  ...  To ensure the CGAN model generated "MR slices" only for scans it was not trained on, 5 CT-To-MR CGANs were created, where each model was trained on 80% of the data and produced derived MR slices for the  ... 
arXiv:1904.13281v1 fatcat:pzrw6xrmfzfivk42smqa7ocsbq

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [article]

Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng Ann Heng
2020 arXiv   pre-print
We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images.  ...  In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with  ...  Specifically, we inject adversarial losses via the semantic prediction space and the generated image space. 1) Feature Alignment in Semantic Prediction Space: as shown in Fig. 2 , for prediction of segmentation  ... 
arXiv:2002.02255v1 fatcat:hwqslvayxnh4tg37gmcso3o37u

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
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  ...  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.  ...  which is a general deep learning model for segmentation of lungs from CT images based on a Generative Adversarial Network (GAN) structure combining the EM distance based loss function.  ... 
arXiv:1901.03473v1 fatcat:3truhntzu5fgtfdwd3gsadbjsa

Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels [chapter]

Pierre-Henri Conze, François Rousseau, Vincent Noblet, Fabrice Heitz, Riccardo Memeo, Patrick Pessaux
2015 Lecture Notes in Computer Science  
By combining SLIC supervoxels and random decision forest, it involves discriminative multi-phase cluster-wise features extracted from registered dynamic contrast-enhanced CT scans.  ...  Toward an efficient evaluation of PLT response, we address the estimation of liver tumor necrosis (TN) from CT scans.  ...  Performing interactive learning and prediction on semantic regions allow tumor segmentation to take advantage of discriminative dynamic information at an extended spatial extent.  ... 
doi:10.1007/978-3-319-24888-2_26 fatcat:aqq4hg2yq5cnra4vsbdu54dxem

Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features [article]

Anindo Saha, Fakrul I. Tushar, Khrystyna Faryna, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo
2020 arXiv   pre-print
The baseline model, with separate stages for segmentation and classification, results in AUC of 0.791.  ...  Using a dataset of 1593 scans labeled on a case-level basis via rule-based model, we train a dual-stage convolutional neural network (CNN) to perform organ segmentation and binary classification of four  ...  We are grateful for GPU equipment from NVIDIA Corporation.  ... 
arXiv:2011.00149v1 fatcat:fy4spbmhc5g4vnqy3powsxrrl4

Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images [article]

Sungho Suh, Sojeong Cheon, Wonseo Choi, Yeon Woong Chung, Won-Kyung Cho, Ji-Sun Paik, Sung Eun Kim, Dong-Jin Chang, Yong Oh Lee
2021 arXiv   pre-print
In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumor, when only small sampled CT images are given.  ...  Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation  ...  Domain adaptation methods were employed for the segmentation of optic nerve and orbital tumor from about 50 and 20 samples of orbital CT scans.  ... 
arXiv:2107.00418v1 fatcat:trpbxwjwwvhy3exyattly7c7te

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules [chapter]

Xinyang Feng, Jie Yang, Andrew F. Laine, Elsa D. Angelini
2017 Lecture Notes in Computer Science  
By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and  ...  Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis.  ...  Acknowledgments Thanks NIH R01-HL121270 for funding.  ... 
doi:10.1007/978-3-319-66179-7_65 pmid:29308456 pmcid:PMC5753796 fatcat:i2k2pqzdercojj6rmbyzdpdlhu

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-modality Cardiac Segmentation

Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng Ann Heng
2019 IEEE Access  
In this paper, we propose a plug-and-play adversarial domain adaptation network (PnP-AdaNet) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT.  ...  We validate our domain adaptation method on cardiac segmentation in unpaired MRI and CT, with four different anatomical structures.  ...  ACKNOWLEDGMENT (Qi Dou and Cheng Ouyang contributed equally to this work.)  ... 
doi:10.1109/access.2019.2929258 fatcat:u4nuxyrzvzerfbrocgg44t6k5m

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network

Yifan Jiang, Han Chen, M. H. Loew, Hanseok Ko
2020 IEEE journal of biomedical and health informatics  
applications including semantic segmentation and classification.  ...  However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.  ...  During the training stage, the semantic segmentation map of a corresponding CT image is passed to the global-local generator, where the label information from the segmentation map is extracted via down-sampling  ... 
doi:10.1109/jbhi.2020.3042523 pmid:33275588 fatcat:k4mwfnoj3zgm3jrvccn2iianv4

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network [article]

Yifan Jiang, Han Chen, Murray Loew, Hanseok Ko
2020 arXiv   pre-print
applications including semantic segmentation and classification.  ...  However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.  ...  During the training stage, the semantic segmentation map of a corresponding CT image is passed to the global-local generator, where the label information from the segmentation map is extracted via down-sampling  ... 
arXiv:2007.14638v2 fatcat:nwaquofzrjeodnuufqjqjbbtpu

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks [article]

Pierre-Henri Conze, Ali Emre Kavur, Emilie Cornec-Le Gall, Naciye Sinem Gezer, Yannick Le Meur, M. Alper Selver, François Rousseau
2020 arXiv   pre-print
In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning.  ...  Conclusion : Combining cascaded convolutional and adversarial networks strengthens the ability of deep learning pipelines to automatically delineate multiple abdominal organs, with good generalization  ...  Towards efficient combined segmentation and based on this unique dataset, we target robust and generic deep learning architectures for two main purposes: 1-segmentation of liver from CT scans and 2-segmentation  ... 
arXiv:2001.09521v1 fatcat:rzfqajt7dbchjcj5ooinzhbkja

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Yu-Huan Wu, Shang-Hua Gao, Jie Mei, Jun Xu, Deng-Ping Fan, Rong-Guo Zhang, Ming-Ming Cheng
2021 IEEE Transactions on Image Processing  
Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation.  ...  To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 chest  ...  Fig. 4 . 4 Combination of the segmentation and classification models. We combine the encoder features of the segmentation model with the backbone features of the classification model.  ... 
doi:10.1109/tip.2021.3058783 fatcat:3bjnuz2abzdhzb3hpn2fdkviem

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea
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