Automatic vertebrae localization from CT scans using volumetric descriptors
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The localization and identification of vertebrae in spinal CT images plays an important role in many clinical applications, such as spinal disease diagnosis, surgery planning, and post-surgery assessment. However, automatic vertebrae localization presents numerous challenges due to partial visibility, appearance similarity of different vertebrae, varying data quality, and the presence of pathologies. Most existing methods require prior information on which vertebrae are present in a scan, and perform poorly on pathological cases, making them of little practical value. In this paper we describe three novel types of local information descriptors which are used to build more complex contextual features, and train a random forest classifier. The three features are progressively more complex, systematically addressing a greater number of limitations of the current state of the art.
Karsten , J & Arandelovic , O 2017 , Automatic vertebrae localization from CT scans using volumetric descriptors . in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) . , 8036890 , IEEE , pp. 576-579 , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 , Jeju Island , Korea, Democratic People's Republic of , 11/07/17 . DOI: 10.1109/EMBC.2017.8036890conference
2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
© 2017, IEEE. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/EMBC.2017.8036890
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