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David Major, J. Hladůvka, F. Schulze, K. Bühler (2013)
Automated landmarking and labeling of fully and partially scanned spinal columns in CT imagesMedical image analysis, 17 8
Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, R. Summers (2015)
Interleaved text/image Deep Mining on a large-scale radiology database2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
HC Shin, HR Roth, M Gao (2016)
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learningMedical Imaging, IEEE Transactions on, 35
C Szegedy, A Toshev, D Erhan (2013)
Advances in Neural Information Processing Systems
AB Oktay, YS Akgul (2013)
Simultaneous localization of lumbar vertebrae and intervertebral discs with svm-based mrfBiomedical Engineering, IEEE Transactions on, 60
Christian Szegedy, Alexander Toshev, D. Erhan (2013)
Deep Neural Networks for Object Detection
Yoshua Bengio, Aaron Courville, Pascal Vincent (2012)
Representation Learning: A Review and New PerspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence, 35
(2016)
Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Nima Tajbakhsh, Jae Shin, S. Gurudu, R. Hurst, Christopher Kendall, M. Gotway, Jianming Liang (2016)
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?IEEE Transactions on Medical Imaging, 35
Daniele Volpi, Mhd Sarhan, R. Ghotbi, Nassir Navab, D. Mateus, S. Demirci (2015)
Online tracking of interventional devices for endovascular aortic repairInternational Journal of Computer Assisted Radiology and Surgery, 10
Y. Zhan, B. Jian, D. Maneesh, X. Zhou (2015)
Cross-Modality Vertebrae Localization and Labeling Using Learning-Based Approaches
Hao Chen, C. Shen, Jing Qin, Dong Ni, Lin Shi, J. Cheng, P. Heng (2015)
Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)
Deep LearningNature, 521
B Glocker, D Zikic, E Konukoglu, DR Haynor, A Criminisi (2013)
Medical image computing and computer-assisted intervention – MICCAI 2013
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, A. Belikov, A. Belopolsky, Yoshua Bengio, Arnaud Bergeron, J. Bergstra, Valentin Bisson, Josh Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, A. Brébisson, Olivier Breuleux, P. Carrier, Kyunghyun Cho, J. Chorowski, P. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, S. Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, S. Kahou, D. Erhan, Ziye Fan, Orhan Firat, M. Germain, Xavier Glorot, I. Goodfellow, M. Graham, Çaglar Gülçehre, P. Hamel, Iban Harlouchet, J. Heng, Balázs Hidasi, S. Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, S. Lee, S. Lefrançois, S. Lemieux, Nicholas Léonard, Zhouhan Lin, J. Livezey, C. Lorenz, J. Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, R. McGibbon, R. Memisevic, B. Merrienboer, Vincent Michalski, Mehdi Mirza, A. Orlandi, C. Pal, Razvan Pascanu, M. Pezeshki, Colin Raffel, D. Renshaw, M. Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, J. Salvatier, F. Savard, Jan Schlüter, J. Schulman, Gabriel Schwartz, Iulian Serban, Dmitriy Serdyuk, S. Shabanian, Étienne Simon, Sigurd Spieckermann, S. Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs Tulder, Joseph Turian, S. Urban, Pascal Vincent, Francesco Visin, Harm Vries, David Warde-Farley, Dustin Webb, M. Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang (2016)
Theano: A Python framework for fast computation of mathematical expressionsArXiv, abs/1605.02688
Yunliang Cai, S. Osman, Manas Sharma, M. Landis, S. Li (2015)
Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical ModelIEEE Transactions on Medical Imaging, 34
Ben Glocker, J. Feulner, A. Criminisi, D. Haynor, E. Konukoglu (2012)
Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT ScansMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 Pt 3
Hoo-Chang Shin, H. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, D. Mollura, R. Summers (2016)
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer LearningIeee Transactions on Medical Imaging, 35
Yoshua Bengio (2007)
Learning Deep Architectures for AIFound. Trends Mach. Learn., 2
SH Huang, YH Chu, SH Lai, CL Novak (2009)
Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRIMedical Imaging, IEEE Transactions on 2009, 28
A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. Riel, M. Wille, Matiullah Naqibullah, C. Sánchez, B. Ginneken (2016)
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional NetworksIEEE Transactions on Medical Imaging, 35
H Greenspan, B Ginneken, RM Summers (2016)
Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new techniqueIEEE Transactions on Medical Imaging, 35
Szu-Hao Huang, Yi-Hong Chu, S. Lai, C. Novak (2009)
Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRIIEEE Transactions on Medical Imaging, 28
Zhennan Yan, Y. Zhan, Zhigang Peng, Shu Liao, Y. Shinagawa, Shaoting Zhang, Dimitris Metaxas, X. Zhou (2016)
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart RecognitionIEEE Transactions on Medical Imaging, 35
T. Klinder, J. Ostermann, M. Ehm, A. Franz, Reinhard Kneser, C. Lorenz (2009)
Automated model-based vertebra detection, identification, and segmentation in CT imagesMedical image analysis, 13 3
Sérgio Pereira, Adriano Pinto, Victor Alves, Carlos Silva (2016)
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI ImagesIEEE Transactions on Medical Imaging, 35
E. Dijkstra (1959)
A note on two problems in connexion with graphsNumerische Mathematik, 1
A. Oktay, Y. Akgul (2013)
Simultaneous Localization of Lumbar Vertebrae and Intervertebral Discs With SVM-Based MRFIEEE Transactions on Biomedical Engineering, 60
A. Suzani, A. Seitel, Yuan Liu, S. Fels, R. Rohling, P. Abolmaesumi (2015)
Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach
Yunliang Cai, M. Landis, D. Laidley, A. Kornecki, Andrea Lum, S. Li (2016)
Multi-modal vertebrae recognition using Transformed Deep Convolution NetworkComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 51
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
Meelis Lootus, T. Kadir, Andrew Zisserman (2014)
Vertebrae Detection and Labelling in Lumbar MR Images
Ben Glocker, D. Zikic, E. Konukoglu, D. Haynor, A. Criminisi (2013)
Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse AnnotationsMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 Pt 2
The purpose of this study was to investigate the potential of using clinically provided spine label annotations stored in a single institution image archive as training data for deep learning-based vertebral detection and labeling pipelines. Lumbar and cervical magnetic resonance imaging cases with annotated spine labels were identified and exported from an image archive. Two separate pipelines were configured and trained for lumbar and cervical cases respectively, using the same setup with convolutional neural networks for detection and parts-based graphical models to label the vertebrae. The detection sensitivity, precision and accuracy rates ranged between 99.1–99.8, 99.6–100, and 98.8–99.8% respectively, the average localization error ranges were 1.18–1.24 and 2.38–2.60 mm for cervical and lumbar cases respectively, and with a labeling accuracy of 96.0–97.0%. Failed labeling results typically involved failed S1 detections or missed vertebrae that were not fully visible on the image. These results show that clinically annotated image data from one image archive is sufficient to train a deep learning-based pipeline for accurate detection and labeling of MR images depicting the spine. Further, these results support using deep learning to assist radiologists in their work by providing highly accurate labels that only require rapid confirmation.
Journal of Digital Imaging – Springer Journals
Published: Jan 12, 2017
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