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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
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T. Heimann, et al., Comparison and evaluation of methods for liver segmentation from ct datasets, IEEE Transactions on Medical Imaging 28 (8) (2009) 1251–1265 · 2009
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J. Ferlay, H.-R. Shin, F. Bray, D. Forman, C. Mathers, D. M. Parkin, Estimates of worldwide burden of cancer in 2008: Globocan 2008, International Journal of Cancer 127 (12) (2010) 2893–2917
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N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, J. C. Gee, N4ITK: Improved N3 bias correction, IEEE Transactions on Medical Imaging 29 (6) (2010) 1310–1320 · 2010
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A. Top, G. Hamarneh, R. Abugharbieh, Spotlight: Automated confidence-based user guidance for increasing efficiency in interactive 3d image segmentation, in: MICCAI, 2010, pp. 204–213
2010
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M. Freiman, O. Cooper, D. Lischinski, L. Joskowicz, Liver tumors segmentation from cta images using voxels classification and affinity constraint propagation, International Journal of Computer Assisted Radiology and Surgery 6 (2) (2011) 247–255
2011
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P. Krähenbühl, V. Koltun, Efficient inference in fully connected crfs with gaussian edge potentials, in: NIPS, 2011, pp. 109–117
2011
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A. Top, G. Hamarneh, R. Abugharbieh, Active learning for interactive 3d image segmentation, in: MICCAI, Vol. 6893, 2011, pp. 603–610
2011
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European Association For The Study Of The Liver, Easl–eortc clinical practice guidelines: management of hepatocellular carcinoma, Journal of Hepatology 56 (4) (2012) 908–943
2012
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Y. Häme, M. Pollari, Semi-automatic liver tumor segmentation with hidden markov measure field model and non-parametric distribution estimation, Medical Image Analysis 16 (1) (2012) 140–149
2012
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A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: NIPS, 2012, pp. 1097–1105
2012
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L. Soler, A. Hostettler, V. Agnus, A. Charnoz, J. Fasquel, J. Moreau, A. Osswald, M. Bouhadjar, J. Marescaux, 3d image reconstruction for comparison of algorithm database: a patient-specific anatomical and medical image database (2012). URL http://www-sop.inria.fr/geometrica/events/wam/abstract-ircad.pdf
2012
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M. Goryawala, M. R. Guillen, M. Cabrerizo, A. Barreto, S. Gulec, T. C. Barot, R. R. Suthar, R. N. Bhatt, A. Mcgoron, M. Adjouadi, A 3-d liver segmentation method with parallel computing for selective internal radiation therapy, Transactions on Information Technology in Biomedicine 16 (1) (2012) 62–69
2012
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C. Li, X. Wang, S. Eberl, M. Fulham, Y. Yin, J. Chen, D. D. Feng, A likelihood and local constraint level set model for liver tumor segmentation from ct volumes, Biomedical Engineering, IEEE Transactions on 60 (10) (2013) 2967–2977
R. Vivanti, A. Ephrat, L. Joskowicz, N. Lev-Cohain, O. A. Karaaslan, J. Sosna, Automatic liver tumor segmentation in follow-up ct scans: Preliminary method and results, in: International Workshop on Patch-based Techniques in Medical Imaging, Springer, 2015, pp. 54–61
2015
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O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: MICCAI, Vol. 9351, 2015, pp. 234–241
2015
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J. Wang, J. D. MacKenzie, R. Ramachandran, D. Z. Chen, Detection of glands and villi by collaboration of domain knowledge and deep learning, in: MICCAI, 2015, pp. 20–27
2015
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H. R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E. B. Turkbey, R. M. Summers, Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation, in: MICCAI, 2015, pp. 556–564
2015
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2013
Cited alongside, same era.
A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, M. Nielsen, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, in: MICCAI, Vol. 16, 2013, pp. 246–253
2013
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Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: Convolutional architecture for fast feature embedding, in: Proceedings of the ACM International Conference on Multimedia, ACM, 2014, pp. 675–678
2014
Cited alongside, same era.
G. Chartrand, T. Cresson, R. Chav, A. Gotra, A. Tang, J. DeGuise, Semi-automated liver ct segmentation using laplacian meshes, in: ISBI, IEEE, 2014, pp. 641–644
2014
Cited alongside, same era.
2014
Cited alongside, same era.
G. Li, X. Chen, F. Shi, W. Zhu, J. Tian, D. Xiang, Automatic liver segmentation based on shape constraints and deformable graph cut in ct images, Image Processing, IEEE Transactions on 24 (12) (2015) 5315–5329
2015
Cited alongside, same era.
A. H. Foruzan, Y.-W. Chen, Improved segmentation of low-contrast lesions using sigmoid edge model, International Journal of Computer Assisted Radiology and Surgery (2015) 1–17
2015
Cited alongside, same era.
S. Kadoury, E. Vorontsov, A. Tang, Metastatic liver tumour segmentation from discriminant grassmannian manifolds, Physics in Medicine and Biology 60 (16) (2015) 6459
2015
Cited alongside, same era.
A. Ben-Cohen, E. Klang, I. Diamant, N. Rozendorn, M. M. Amitai, H. Greenspan, Automated method for detection and segmentation of liver metastatic lesions in follow-up ct examinations, Journal of Medical Imaging (3)
Cited in the paper.
M. F. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber, Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation, in: Advances in Neural Information Processing Systems, 2015, pp. 2998–3006
2015
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F. López-Mir, P. González, V. Naranjo, E. Pareja, S. Morales, J. Solaz-Mínguez, A method for liver segmentation on computed tomography images in venous phase suitable for real environments, Journal of Medical Imaging and Health Informatics 5 (6) (2015) 1208–1216
2015
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P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S.-A. Ahmadi, B. H. Menze, Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields, MICCAI, Cham, 2016, pp. 415–423
2016
Later among the works it cites.
F. Milletari, N. Navab, S.-A. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 565–571
2016
Later among the works it cites.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger, 3d u-net: learning dense volumetric segmentation from sparse annotation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2016, pp. 424–432
2016
Later among the works it cites.
A. Ben-Cohen, I. Diamant, E. Klang, M. Amitai, H. Greenspan, Fully convolutional network for liver segmentation and lesions detection, in: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, Springer, 2016, pp. 77–85
2016
Later among the works it cites.
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, B. Glocker, Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation, Medical Image Analysis 36 (2017) 61–78
2017
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