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references-1a.bib
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title = {{101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol}},
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title = {{3D alpha matting based co-segmentation of tumors on PET-CT images}},
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volume = {10555 LNCS},
isbn = {9783319675633},
doi = {10.1007/978-3-319-67564-0{\_}4},
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keywords = {Co-segmentation, Image matting, Image segmentation, Interactive segmentation, Lung tumor segmentation}
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title = {{3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies}},
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publisher = {Springer International Publishing},
address = {Cham},
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title = {{3D fully convolutional networks for co-segmentation of tumors on PET-CT images}},
year = {2018},
booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
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month = {4},
pages = {228--231},
doi = {10.1109/ISBI.2018.8363561},
keywords = {3D fully convolutional networks, Biomedical imaging, Computed tomography, Image segmentation, Lung, PET-CT images, PET-CT scans, Three-dimensional displays, Tumors, automated accurate tumor delineation, biomedical MRI, cancer, cancer diagnosis, co-segmentation, co-segmentation model, computed tomography, computerised tomography, critical diagnostic information, deep learning, dual-modality imaging, final tumor segmentation results, fully convolutional networks, graph cut, image classification, image segmentation, learning (artificial intelligence), lung, lung cancer patients, lung tumor segmentation, medical image processing, positron emission tomography, probability maps, semantic segmentation framework, tumor reading, tumours}
}
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title = {{3D scene understanding by voxel-CRF}},
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url = {https://doi.org/10.1109/ICCV.2013.180},
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doi = {10.1109/ICCV.2013.180},
keywords = {3D reconstruction, RGB-D, Scene understanding}
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title = {{3D ShapeNets: A deep representation for volumetric shapes}},
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isbn = {9781467369640},
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title = {{A comparison study of different color spaces in clustering based image segmentation}},
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publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
isbn = {9783642140570},
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keywords = {CMY, Clustering, Color space, HSV, Image segmentation, RGB, YUV}
}
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title = {{A Comprehensive Survey of Video Datasets for Background Subtraction}},
year = {2019},
booktitle = {IEEE Access},
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pages = {59143--59171},
volume = {7},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
doi = {10.1109/ACCESS.2019.2914961},
issn = {21693536},
keywords = {Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames}
}
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title = {{A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography}},
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url = {http://arxiv.org/abs/1706.07342},
arxivId = {1706.07342}
}
@article{Kumar2017,
title = {{A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology}},
year = {2017},
journal = {IEEE Transactions on Medical Imaging},
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number = {7},
month = {7},
pages = {1550--1560},
volume = {36},
doi = {10.1109/TMI.2017.2677499},
issn = {1558254X},
keywords = {Annotation, boundaries, dataset, deep learning, nuclear segmentation, nuclei}
}
@article{Zhao2018,
title = {{A deep learning model integrating FCNNs and CRFs for brain tumor segmentation}},
year = {2018},
journal = {Medical Image Analysis},
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pages = {98 – 111},
volume = {43},
url = {http://www.sciencedirect.com/science/article/pii/S136184151730141X},
doi = {https://doi.org/10.1016/j.media.2017.10.002},
issn = {1361-8415},
keywords = {Brain tumor segmentation, Conditional random fields, Deep learning, Fully convolutional neural networks}
}
@inproceedings{Zhou2017,
title = {{A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans}},
year = {2017},
booktitle = {Medical Image Computing and Computer Assisted Intervention − MICCAI 2017},
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pages = {693--701},
publisher = {Springer International Publishing},
address = {Cham},
isbn = {978-3-319-66182-7}
}
@article{Jimenez-Carretero2019,
title = {{A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images}},
year = {2019},
journal = {Medical Image Analysis},
author = {Jimenez-Carretero, Daniel and Bermejo-Pel{\'{a}}ez, David and Nardelli, Pietro and Fraga, Patricia and Fraile, Eduardo and San Jos{\'{e}} Est{\'{e}}par, Raúl and Ledesma-Carbayo, Maria J},
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volume = {52},
url = {http://www.sciencedirect.com/science/article/pii/S1361841518308740},
doi = {10.1016/j.media.2018.11.011},
issn = {13618423},
keywords = {Arteries, Artery-vein segmentation, Graph-cuts, Lung, Noncontrast CT, Phantoms, Random forest, Veins}
}
@article{Liew2018,
title = {{A large, open source dataset of stroke anatomical brain images and manual lesion segmentations}},
year = {2018},
journal = {Scientific Data},
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month = {12},
pages = {180011},
volume = {5},
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title = {{A reproducible evaluation of ANTs similarity metric performance in brain image registration}},
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institution = {Department of Computer Science National Taiwan University, Taipei 106, Taiwan},
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@article{Jeyavathana2016,
title = {{A Survey : Analysis on Pre - processing and Segmentation Techniques for Medical Images}},
year = {2016},
journal = {International Journal of Research and Scientific Innovation},
author = {Beaulah Jeyavathana, R and Balasubramanian, R and Pandian, A Anbarasa},
number = {June},
pages = {2321--2705},
volume = {III},
keywords = {CT, CXR, Pre - processing, Segmentation}
}
@inproceedings{Lee2015,
title = {{A survey of medical image processing tools}},
year = {2015},
booktitle = {2015 4th International Conference on Software Engineering and Computer Systems, ICSECS 2015: Virtuous Software Solutions for Big Data},
author = {Lee, Lay Khoon and Liew, Siau Chuin},
pages = {171--176},
isbn = {9781467367226},
doi = {10.1109/ICSECS.2015.7333105},
keywords = {computer vision, image processing, tools component}
}
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title = {{A Survey of Visualization Tools in Medical Imaging}},
year = {2012},
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url = {http://www.sciencedirect.com/science/article/pii/S187704281204116X},
doi = {10.1016/j.sbspro.2012.09.654},
issn = {18770428},
keywords = {Image editting, Image processing, Medical imaging, Software package, Visualisation tools}
}
@misc{Litjens2017,
title = {{A survey on deep learning in medical image analysis}},
year = {2017},
booktitle = {Medical Image Analysis},
author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and van der Laak, Jeroen A.W.M. and van Ginneken, Bram and S{\'{a}}nchez, Clara I},
pages = {60--88},
volume = {42},
url = {http://www.sciencedirect.com/science/article/pii/S1361841517301135},
doi = {10.1016/j.media.2017.07.005},
issn = {13618423},
pmid = {28778026},
arxivId = {1702.05747},
keywords = {Convolutional neural networks, Deep learning, Medical imaging, Survey}
}
@article{Shorten2019,
title = {{A survey on Image Data Augmentation for Deep Learning}},
year = {2019},
journal = {Journal of Big Data},
author = {Shorten, Connor and Khoshgoftaar, Taghi M},
number = {1},
pages = {60},
volume = {6},
url = {https://doi.org/10.1186/s40537-019-0197-0},
doi = {10.1186/s40537-019-0197-0},
issn = {21961115},
keywords = {Big data, Data Augmentation, Deep Learning, GANs, Image data}
}
@article{Yao2017,
title = {{A Survey on Pre-Processing in Image Matting}},
year = {2017},
journal = {Journal of Computer Science and Technology},
author = {Yao, Gui Lin},
number = {1},
pages = {122--138},
volume = {32},
url = {https://doi.org/10.1007/s11390-017-1709-z},
doi = {10.1007/s11390-017-1709-z},
issn = {10009000},
keywords = {Trimap expansion, image matting, pixel classification, pre-processing}
}
@article{Hindy2018,
title = {{A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets}},
year = {2018},
author = {Hindy, Hanan and Brosset, David and Bayne, Ethan and Seeam, Amar and Tachtatzis, Christos and Atkinson, Robert and Bellekens, Xavier},
month = {6},
url = {http://arxiv.org/abs/1806.03517},
arxivId = {1806.03517}
}
@article{Bakas2017,
title = {{Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features}},
year = {2017},
journal = {Scientific Data},
author = {Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, Justin S. and Freymann, John B. and Farahani, Keyvan and Davatzikos, Christos},
number = {1},
month = {12},
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