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Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline.
Automated design of deep learning methods for biomedical image segmentation
Isensee, F., Jäger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2019) · 1904
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The thoracolumbar and lumbosacral transitional junctions
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Risk of mortality following clinical fractures
Cauley, J., Thompson, D., Ensrud, K., Scott, J., & Black, D. (2000) · 2000
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Statistical shape influence in geodesic active contours
Leventon, M. E., Grimson, W. E. L., & Faugeras, O. (2002) · 2002
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Significance of sagittal reformations in routine thoracic and abdominal multislice ct studies for detecting osteoporotic fractures and other spine abnormalities
Müller, D., Bauer, J. S., Zeile, M., Rummeny, E. J., & Link, T. M. (2008) · 2008
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Automatic segmentation of the pelvic bones from ct data based on a statistical shape model
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Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. P. (2009) · 2009
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Automated model-based vertebra detection, identification, and segmentation in ct images
Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C. (2009) · 2009
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Under-reporting of osteoporotic vertebral fractures on computed tomography
Williams, A. L., Al-Busaidi, A., Sparrow, P. J., Adams, J. E., & Whitehouse, R. W. (2009) · 2009
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Automatic inference of articulated spine models in ct images using high-order markov random fields
Kadoury, S., Labelle, H., & Paragios, N. (2011) · 2011
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Classification of normal sagittal spine alignment: refounding the roussouly classification
Laouissat, F., Sebaaly, A., Gehrchen, M., & Roussouly, P. (2018) · 2011
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Parametric modelling and segmentation of vertebral bodies in 3d ct and mr spine images
Štern, D., Likar, B., Pernuš, F., & Vrtovec, T. (2011) · 2011
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Automatic localization and identification of vertebrae in arbitrary field-of-view ct scans
Glocker, B., Feulner, J., Criminisi, A., Haynor, D. R., & Konukoglu, E. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012) · 2012
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Detection of vertebral body fractures based on cortical shell unwrapping
Yao, J., Burns, J. E., Munoz, H., & Summers, R. M. (2012) · 2012
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Vertebrae localization in pathological spine ct via dense classification from sparse annotations
Glocker, B., Zikic, D., Konukoglu, E., Haynor, D. R., & Criminisi, A. (2013) · 2013
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Spine segmentation in medical images using manifold embeddings and higher-order mrfs
Kadoury, S., Labelle, H., & Paragios, N. (2013) · 2013
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Automated landmarking and labeling of fully and partially scanned spinal columns in ct images
Major, D., Hladŭvka, J., Schulze, F., & Bühler, K. (2013) · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014
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Shape representation for efficient landmark-based segmentation in 3-d
Ibragimov, B., Likar, B., Pernuš, F., & Vrtovec, T. (2014) · 2014
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A robust segmentation framework for spine trauma diagnosis
Lim, P. H., Bagci, U., & Bai, L. (2014) · 2014
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Fast parallel image registration on cpu and gpu for diagnostic classification of alzheimer’s disease
Shamonin, D. P., Bron, E. E., Lelieveldt, B. P., Smits, M., Klein, S., & Staring, M. (2014) · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., & Zisserman, A. (2014) · 2014
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Multi-modality vertebra recognition in arbitrary views using 3d deformable hierarchical model
Cai, Y., Osman, S., Sharma, M., Landis, M., & Li, S. (2015) · 2015
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Statistical interspace models (sims): application to robust 3d spine segmentation
Castro-Mateos, I., Pozo, J. M., Pereañez, M., Lekadir, K., Lazary, A., & Frangi, A. F. (2015) · 2015
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Automatic localization and identification of vertebrae in spine ct via a joint learning model with deep neural networks
Chen, H., Shen, C., Qin, J., Ni, D., Shi, L., Cheng, J. C., & Heng, P.-A. (2015) · 2015
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Fully automatic localization and segmentation of 3d vertebral bodies from ct/mr images via a learning-based method
Chu, C., Belavỳ, D. L., Armbrecht, G., Bansmann, M., Felsenberg, D., & Zheng, G. (2015) · 2015
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Fast r-cnn
Girshick, R. (2015) · 2015
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Vertebrae segmentation in 3d ct images based on a variational framework
Hammernik, K., Ebner, T., Stern, D., Urschler, M., & Pock, T. (2015) · 2015
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Interpolation-based detection of lumbar vertebrae in ct spine images
Ibragimov, B., Korez, R., Likar, B., Pernuš, F., & Vrtovec, T. (2015) · 2015
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A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation
Korez, R., Ibragimov, B., Likar, B., Pernuš, F., & Vrtovec, T. (2015) · 2015
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Accurate segmentation of vertebral bodies and processes using statistical shape decomposition and conditional models
Pereañez, M., Lekadir, K., Castro-Mateos, I., Pozo, J. M., Lazáry, Á., & Frangi, A. F. (2015) · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2018) · 2018
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Why rankings of biomedical image analysis competitions should be interpreted with care
Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A. et al. (2018) · 2018
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Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks
Roy, A. G., Navab, N., & Wachinger, C. (2018) · 2018
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Btrfly net: Vertebrae labelling with energy-based adversarial learning of local spine prior
Sekuboyina, A., Rempfler, M., Kukačka, J., Tetteh, G., Valentinitsch, A., Kirschke, J. S., & Menze, B. H. (2018) · 2018
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Group normalization
Wu, Y., & He, K. (2018) · 2018
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Projection-based 2.5 d u-net architecture for fast volumetric segmentation
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Ren, S., He, K., Girshick, R., & Sun, J. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. et al. (2015) · 2015
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Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric mr images
Suzani, A., Rasoulian, A., Seitel, A., Fels, S., Rohling, R. N., & Abolmaesumi, P. (2015a) · 2015
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Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool
Taha, A. A., & Hanbury, A. (2015) · 2015
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Automatic segmentation of vertebral contours from ct images using fuzzy corners
Athertya, J. S., & Kumar, G. S. (2016) · 2016
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Fully automatic localisation of vertebrae in ct images using random forest regression voting
Bromiley, P. A., Kariki, E. P., Adams, J. E., & Cootes, T. F. (2016) · 2016
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Angermann, C., Haltmeier, M., Steiger, R., Pereverzyev, S., & Gizewski, E. (2019) · 2019
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Lsrc: A long-short range context-fusing framework for automatic 3d vertebra localization
Chen, J., Wang, Y., Guo, R., Yu, B., Chen, T., Wang, W., Feng, R., Chen, D. Z., & Wu, J. (2019) · 2019
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Deep convolutional neural network inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study
Guan, Q., Wan, X., Lu, H., Ping, B., Li, D., Wang, L., Zhu, Y., Wang, Y., & Xiang, J. (2019) · 2019
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Squeeze-and-excitation networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2019) · 2019
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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Lessmann, N., van Ginneken, B., de Jong, P. A., & Išgum, I. (2019) · 2019
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Automatically localizing a large set of spatially correlated key points: A case study in spine imaging
Mader, A. O., Lorenz, C., von Berg, J., & Meyer, C. (2019) · 2019
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Integrating spatial configuration into heatmap regression based cnns for landmark localization
Payer, C., Štern, D., Bischof, H., & Urschler, M. (2019) · 2019
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Deep high-resolution representation learning for human pose estimation
Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019
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Unet++: Redesigning skip connections to exploit multiscale features in image segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2019) · 2019
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Effect of the intervertebral disc on vertebral bone strength prediction: a finite-element study
Anitha, D. P., Baum, T., Kirschke, J. S., & Subburaj, K. (2020) · 2020
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Deep reasoning networks for unsupervised pattern de-mixing with constraint reasoning
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Radiology reporting of osteoporotic vertebral fragility fractures on computed tomography studies: results of a uk national audit
Howlett, D. C., Drinkwater, K. J., Mahmood, N., Illes, J., Griffin, J., & Javaid, K. (2020) · 2020
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A tool for automatic estimation of patient position in spinal ct data
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Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation ct images
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Labeling vertebrae with two-dimensional reformations of multidetector ct images: An adversarial approach for incorporating prior knowledge of spine anatomy
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