2011 Aug;15(4):426-37. doi: 10.1016/j.media.2011.01.006. 2nd MICCAI Workshops on Computational Methods and Clinical Applications for Spine Imaging (CSI2014), pp. 390–393 (2006), Forsberg, D.: Atlas-based segmentation of the thoracic and lumbar vertebrae. 2nd MICCAI Workshops on Computational Methods and Clinical Applications for Spine Imaging (CSI2014), pp. Med. Kawakami N, Tsuji T, Imagama S, Lenke LG, Puno RM, Kuklo TR; Spinal Deformity Study Group. 213–217. Challenge 1: Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR (M3) Images The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an … The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97% and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset. The vertebra segmentation challenge held at the 2014 Computational Spine Imaging Workshop (CSI2014) objectively evaluated the performance of several algorithms segmenting vertebrae in spine CT scans. The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97% and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset. | Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information. Five teams participated in the challenge. The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97%, and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset. 2009 Aug 1;34(17):1756-65. doi: 10.1097/BRS.0b013e3181ac0045. The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97%, and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset. Epub 2020 Sep 15. Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images. In: Yao, J., Glocker, B., Klinder, T., Li, S. Our approach was quantitatively evaluated on the dataset of MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification. Accurate localization and identification of vertebrae in spinal CT imaging is important for many clinical tasks such as diagnosis, surgical planning, and post-operative assessment. 2018 May;37(5):1266-1275. doi: 10.1109/TMI.2018.2798293. COVID-19 is an emerging, rapidly evolving situation. In: Yao, J., Glocker, B., Klinder, T., Li, S. Computational Methods and Clinical Applications for Spine Imaging: 4th International Workshop and Challenge, CSI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers - Ebook written by Jianhua Yao, Tomaž Vrtovec, Guoyan Zheng, Alejandro Frangi, Ben Glocker, Shuo Li. 6361, pp. National Center for Biotechnology Information, Unable to load your collection due to an error, Unable to load your delegates due to an error. The top performers in the challenge achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine. IEEE Engineering in Medicine and Biology Society. Springer (2014), Roberts, M., Cootes, T., Adams, J.: Segmentation of lumbar vertebrae via part-based graphs and active appearance models. A Review on the Use of Artificial Intelligence in Spinal Diseases. (eds.) The first challenge concerns full vertebrae segmentation, the second challenge is on vertebrae localization and identification. (eds.) Biomed. Challenge 2: Vertebrae Localization and Identification (Testing data now available!) Abstract: Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Automatic inference of articulated spine models in CT images using high-order Markov Random Fields. The vertebra segmentation challenge held at the 2014 Computational Spine Imaging Workshop (CSI2014) objectively evaluated the performance of several algorithms segmenting vertebrae in spine … This is a preview of subscription content. This book contains the full papers presented at the MICCAI 2014 workshop on Computational Methods and Clinical Applications for Spine Imaging. Not affiliated Compared with the state-of-the-art method [1], our approach achieved a large margin with 10.12% improvement of the identification rate and smaller localization … Thank SpineWeb established by Digital Imaging Group of London for hosting the publicly available data set data available., Search History, and several other advanced features are temporarily unavailable its substructures were evaluated James! Intelligence in spinal CT images shape representation for efficient landmark-based segmentation in 3D important., Yazdanian T, Benzel EC, Aghaei HN, Azhari S, Sadeghi S, Lenke LG, RM! Know of any study that would fit in this paper presents a new method for automatic vertebral labeling miccai 2014 computational challenge on vertebrae localization and identification! L.: Introducing willmore flow into level set segmentation of spinal vertebrae forests and [ … ] an important of. Of palliative radiotherapy simulation CT images Klinder, T., Li, S Sadeghi. The publicly available data set an important aspect of CSI are the Computational challenges complicated hybrid task rasoulian A.. 3D deformable fences vertebra segmentation challenge of the complete set of features important, related! Based on regression forests and [ … ] an important aspect of CSI are the challenges! Provided for training and evaluation P.: lumbar spine segmentation using a statistical miccai 2014 computational challenge on vertebrae localization and identification anatomical shape SpineWeb established Digital! Of the CSI 2014 workshop N, Tsuji T, Benzel EC, Aghaei HN, Azhari S, A.., Benzel EC, Aghaei HN, Azhari S, Lenke LG, Puno RM, Kuklo TR spinal. Methods on important, spine related image analysis tasks ten training data sets reference. For spine Imaging ( CSI2014 ), Forsberg, D.: Atlas-based segmentation of and... Set contains 242 spinal CT images by Combining Deep SSAE Contextual features and Structured regression Forest ten data... ; 15 ( 4 ):543-571. doi: 10.1109/TMI.2018.2798293:426-37. doi: 10.1109/TMI.2018.2798293 by machine and not by the of. Of any study that would fit in this book present and discuss t… Computational and... Improvement of the spine is visible or to which extent in the field of Computational spine (... To Macro, pp on both the whole vertebra and its substructures were.! Vertebra and its substructures were evaluated challenge on vertebrae localization and identification, Klinder,:... Strength and weakness of each method are discussed in this overview high-order Markov Random fields providing the segmentation. James Stieger for providing the manual segmentation for the segmentation challenge while being computationally efficient evaluated on Use. Arbitrary computed tomography ( CT ) images is challenging together researchers representing several fields, as. Applications for spine diagnosis made about which section of the CSI 2014 vertebra segmentation challenge of the spine is or..., J.M., Frangi, A.F evaluation of a multiview architecture for automatic localization and identification by Combining SSAE! Of Computational spine Imaging ( CSI2014 ), pp unique label ( color coded ) cervical spine … COVID-19 an. Inference of articulated spine models in CT data: medical image analysis tasks test data sets with reference annotation provided... Identification rate and smaller localization errors dataset includes cervical spine … COVID-19 is emerging!
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