Automatic vertebrae localization from CT scans using volumetric descriptors

Juan Karsten, Ognjen Arandelovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
5 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages576-579
DOIs
Publication statusPublished - 14 Sept 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - International Conference Centre (ICC), Jeju Island, Korea, Democratic People's Republic of
Duration: 11 Jul 201715 Jul 2017
Conference number: 38
https://embc.embs.org/2017/

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Abbreviated titleEMBC
Country/TerritoryKorea, Democratic People's Republic of
CityJeju Island
Period11/07/1715/07/17
Internet address

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