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Vertebral body compression fractures are early signs of osteoporosis.
Detection of vertebral fractures in CT using 3D convolutional neural networks
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VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images
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An estimate of the worldwide prevalence and disability associated with osteoporotic fractures
Johnell, O., Kanis, J., 2006 · 2006
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Missed incidental vertebral compression fractures on computed tomography imaging: More optimism justified
Gossner, J., 2010 · 2010
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Vertebral Fracture Initiative: Executive summary
Genant, H.K., Bouxsein, M.L., 2011 · 2011
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Scoliosis in adults aged forty years and older: prevalence and relationship to age, race, and gender
Kebaish, K.M., Neubauer, P.R., Voros, G.D., Khoshnevisan, M.A., Skolasky, R.L., 2011 · 2011
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Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 590–598
Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E., 2012 · 2012
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Intra and interobserver reliability and agreement of semiquantitative vertebral fracture assessment on chest computed tomography
Buckens, C.F., de Jong, P.A., Mol, C., Bakker, E., Stallman, H.P., Mali, W.P., van der Graaf, Y., Verkooijen, H.M., 2013 · 2013
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Vertebrae localization in pathological spine CT via dense classification from sparse annotations, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 262–270
Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A., 2013 · 2013
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Kingma, D.P., Ba, J., 2014 · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks, in: Advances in neural information processing systems, pp. 91–99
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V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 3D Vision (3D V), 2016 Fourth International Conference on, IEEE. pp. 565–571
Milletari, F., Navab, N., Ahmadi, S.A., 2016 · 2016
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Deep convolutional networks for automated detection of posterior-element fractures on spine CT, in: Medical Imaging 2016: Computer-Aided Diagnosis, International Society for Optics and Photonics. p. 97850P
Roth, H.R., Wang, Y., Yao, J., Lu, L., Burns, J.E., Summers, R.M., 2016 · 2016
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Compression fractures detection on CT, in: Medical Imaging 2017: Computer-Aided Diagnosis, International Society for Optics and Photonics. p. 1013440
Bar, A., Wolf, L., Amitai, O.B., Toledano, E., Elnekave, E., 2017 · 2017
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Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures
Valentinitsch, A., Trebeschi, S., Kaesmacher, J., Lorenz, C., Löffler, M., Zimmer, C., Baum, T., Kirschke, J., 2019 · 2019
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Combining deep learning and model-based segmentation for labeled spine CT segmentation, in: Medical Imaging 2020: Image Processing, International Society for Optics and Photonics. p. 113131C
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3D convolutional sequence to sequence model for vertebral compression fractures identification in CT, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Springer International Publishing, Cham. pp. 743–752
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Grading loss: A fracture grade-based metric loss for vertebral fracture detection, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, pp. 733–742
Husseini, M., Sekuboyina, A., Loeffler, M., Navarro, F., Menze, B.H., Kirschke, J.S., 2020 · 2020
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Vertebral body compression fractures and bone density: automated detection and classification on CT images
Burns, J.E., Yao, J., Summers, R.M., 2017 · 2017
Cited alongside, same era.
Value and potential limitations of vertebral fracture assessment (VFA) compared to conventional spine radiography: experience from a fracture liaison service (FLS) and a meta-analysis
Malgo, F., Hamdy, N., Ticheler, C., Smit, F., Kroon, H., Rabelink, T., Dekkers, O., Appelman-Dijkstra, N., 2017 · 2017
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Reporting of vertebral fragility fractures: can radiologists help reduce the number of hip fractures?
Mitchell, R., Jewell, P., Javaid, M., McKean, D., Ostlere, S., 2017 · 2017
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Attention-driven deep learning for pathological spine segmentation, in: International Workshop and Challenge on Computational Methods and Clinical Applications in Musculoskeletal Imaging, Springer. pp. 108–119
Sekuboyina, A., Kukačka, J., Kirschke, J.S., Menze, B.H., Valentinitsch, A., 2017 · 2017
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Vertebra fracture classification from 3D CT lumbar spine segmentation masks using a convolutional neural network, in: Asian Conference on Intelligent Information and Database Systems, Springer. pp. 449–458
Antonio, C.B., Bautista, L.G.C., Labao, A.B., Naval, P.C., 2018 · 2018
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2d/3d pose estimation and action recognition using multitask deep learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5137–5146
Luvizon, D.C., Picard, D., Tabia, H., 2018 · 2018
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Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans
Tomita, N., Cheung, Y.Y., Hassanpour, S., 2018 · 2018
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European guidance for the diagnosis and management of osteoporosis in postmenopausal women
Kanis, J.A., Cooper, C., Rizzoli, R., Reginster, J.Y., 2019 · 2019
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A vertebral segmentation dataset with fracture grading
Löffler, M.T., Sekuboyina, A., Jacob, A., Grau, A.L., Scharr, A., El Husseini, M., Kallweit, M., Zimmer, C., Baum, T., Kirschke, J.S., 2020 · 2020
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Automatic segmentation, localization, and identification of vertebrae in 3D CT images using cascaded convolutional neural networks, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Springer International Publishing, Cham. pp. 681–690
Masuzawa, N., Kitamura, Y., Nakamura, K., Iizuka, S., Simo-Serra, E., 2020 · 2020
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Keypoints localization for joint vertebra detection and fracture severity quantification, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Springer International Publishing, Cham. pp. 723–732
Pisov, M., Kondratenko, V., Zakharov, A., Petraikin, A., Gombolevskiy, V., Morozov, S., Belyaev, M., 2020 · 2020
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A convolutional approach to vertebrae detection and labelling in whole spine MRI, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, pp. 712–722
Windsor, R., Jamaludin, A., Kadir, T., Zisserman, A., 2020 · 2020
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The false hope of current approaches to explainable artificial intelligence in health care
Ghassemi, M., Oakden-Rayner, L., Beam, A.L., 2021 · 2021
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A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans
Morozov, S., Gombolevskiy, V., Elizarov, A., Gusev, M., Novik, V., Prokudaylo, S., Bardin, A., Popov, E., Ledikhova, N., Chernina, V., et al., 2021 · 2021
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Reference medical datasets (MosMedData) for independent external evaluation of algorithms based on artificial intelligence in diagnostics
Pavlov, N.A., Andreychenko, A.E., Vladzymyrskyy, A.V., Revazyan, A.A., Kirpichev, Y.S., Morozov, S.P., 2021 · 2021
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VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images
Sekuboyina, A., Husseini, M.E., Bayat, A., Löffler, M., Liebl, H., Li, H., Tetteh, G., Kukačka, J., Payer, C., Štern, D., et al., 2021 · 2021
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Automated deep learning-based detection of osteoporotic fractures in CT images, in: International Workshop on Machine Learning in Medical Imaging, Springer. pp. 376–385
Yilmaz, E.B., Buerger, C., Fricke, T., Sagar, M.M.R., Peña, J., Lorenz, C., Glüer, C.C., Meyer, C., 2021 · 2021
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