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In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs).
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S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, H. S. Seung, Convolutional networks can learn to generate affinity graphs for image segmentation, Neural Comput 22 (2) (2010) 511–538
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D. Berg, K. Seppi, S. Behnke, I. Liepelt, K. Schweitzer, H. Stockner, F. Wollenweber, A. Gaenslen, P. Mahlknecht, J. Spiegel, J. Godau, H. Huber, K. Srulijes, S. Kiechl, M. Bentele, A. Gasperi, T. Schubert, T. Hiry, M. Probst, V. Schneider, J. Klenk, M. Sawires, J. Willeit, W. Maetzler, K. Fassbender, T. Gasser, W. Poewe, Enlarged substantia nigra hyperechogenicity and risk for Parkinson disease: a 37-month 3-center study of 1847 older persons, Arch. Neurol. 68 (7) (2011) 932–937
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S. A. Ahmadi, M. Baust, A. Karamalis, A. Plate, K. Bötzel, T. Klein, N. Navab, Midbrain segmentation in transcranial 3D ultrasound for Parkinson diagnosis, Med Image Comput Comput Assist Interv 14 (Pt 3) (2011) 362–369
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N. Lee, A. Laine, A. Klein, Towards a deep learning approach to brain parcellation, in: Biomedical Imaging: From Nano to Macro, 2011 IEEE Intl. Symp. on, 2011, pp. 321–324
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B. Patenaude, S. M. Smith, D. N. Kennedy, M. Jenkinson, A bayesian model of shape and appearance for subcortical brain segmentation, NeuroImage 56 (3) (2011) 907 – 922
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A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105
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P.-F. D’Haese, S. Pallavaram, R. Li, M. S. Remple, C. Kao, J. S. Neimat, P. E. Konrad, B. M. Dawant, Cranialvault and its crave tools: A clinical computer assistance system for deep brain stimulation (dbs) therapy, Medical Image Analysis 16 (3) (2012) 744–753
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C. Szegedy, A. Toshev, D. Erhan, Deep neural networks for object detection, in: Advances in Neural Information Processing Systems 26, 2013, pp. 2553–2561
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D. Cireşan, A. Giusti, L. Gambardella, J. Schmidhuber, Mitosis detection in breast cancer histology images with deep neural networks, in: Med Image Comput Comput Assist Interv, Vol. 8150, 2013, pp. 411–418
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T. A. Ngo, G. Carneiro, Left ventricle segmentation from cardiac mri combining level set methods with deep belief networks, in: Image Processing (ICIP), IEEE Intl. Conf. on, 2013, pp. 695–699
2013
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D. Eigen, C. Puhrsch, R. Fergus, Depth map prediction from a single image using a multi-scale deep network, in: Advances in Neural Information Processing Systems, 2014, pp. 2366–2374
2014
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R. Ranftl, T. Pock, A deep variational model for image segmentation, in: Pattern Recognition, Vol. 8753, 2014, pp. 107–118
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Computer Vision and Pattern Recognition, IEEE Conf. on, 2015
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Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436–444
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T. D’Albis, C. Haegelen, C. Essert, S. Fernández-Vidal, F. Lalys, P. Jannin, Pydbs: an automated image processing workflow for deep brain stimulation surgery, International Journal of Computer Assisted Radiology and Surgery 10 (2) (2015) 117–128
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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, Med Image Comput Comput Assist Interv 16 (Pt 2) (2013) 246–253
2013
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G. Riegler, D. Ferstl, M. Rüther, H. Bischof, Hough networks for head pose estimation and facial feature localization, Journal of Computer Vision 101 (3) (2013) 437–458
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M. Kim, G. Wu, D. Shen, Unsupervised deep learning for hippocampus segmentation in 7.0 tesla mr images, in: Machine Learning in Medical Imaging, Vol. 8184, 2013, pp. 1–8
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M. T. Barbe, T. A. Dembek, J. Becker, J. Raethjen, M. Hartinger, I. G. Meister, M. Runge, M. Maarouf, G. R. Fink, L. Timmermann, Individualized current-shaping reduces dbs-induced dysarthria in patients with essential tremor, Neurology 82 (7) (2014) 614–619
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H. Roth, L. Lu, A. Seff, K. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, R. Summers, A new 2.5d representation for lymph node detection using random sets of deep convolutional neural network observations, in: Med Image Comput Comput Assist Interv, Vol. 8673, 2014, pp. 520–527
2014
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R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in: Computer Vision and Pattern Recognition, IEEE Conf. on, 2014, pp. 580–587
2014
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2015
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F. Milletari, S.-A. Ahmadi, C. Kroll, C. Hennersperger, F. Tombari, A. Shah, A. Plate, K. Boetzel, N. Navab, Robust segmentation of various anatomies in 3d ultrasound using hough forests and learned data representations, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, Springer, 2015, pp. 111–118
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Y. Xie, X. Kong, F. Xing, F. Liu, H. Su, L. Yang, Deep voting: A robust approach toward nucleus localization in microscopy images, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, Springer, 2015, pp. 374–382
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Y. Song, L. Zhang, S. Chen, D. Ni, B. Lei, T. Wang, Accurate segmentation of cervical cytoplasm and nuclei based on multi-scale convolutional network and graph partitioning, Biomedical Engineering, IEEE Trans. on PP (99) (2015) 1–1
2015
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O. Dietrich, S.-A. Ahmadi, J. Levin, J. Maiostre, A. Plate, A. Giese, K. Bötzel, M. F. Reiser, B. Ertl-Wagner, Quantitative susceptibility mapping with superfast dipole inversion: Influence of regularization parameters on the susceptibility of the substantia nigra and the red nucleus, in: Proc. Intl. Soc. Mag. Reson. Med., Vol. 23, 2015, p. 3325
2015
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A. Plate, S. A. Ahmadi, O. Pauly, T. Klein, N. Navab, K. Bötzel, Three-dimensional sonographic examination of the midbrain for computer-aided diagnosis of movement disorders, Ultrasound Med Biol 38 (12) (2012) 2041–2050
2050
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