Multiple hypothesis tracking based extraction of airway trees from CT data: using statistical ranking of template-matched hypotheses
Publikation: Konferencebidrag › Poster › Forskning › fagfællebedømt
Standard
Multiple hypothesis tracking based extraction of airway trees from CT data : using statistical ranking of template-matched hypotheses. / Raghavendra, Selvan; Petersen, Jens; de Bruijne, Marleen.
2016. Poster session præsenteret ved Medical Imaging Summer School 2016, Italien.Publikation: Konferencebidrag › Poster › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - CONF
T1 - Multiple hypothesis tracking based extraction of airway trees from CT data
T2 - Medical Imaging Summer School 2016
AU - Raghavendra, Selvan
AU - Petersen, Jens
AU - de Bruijne, Marleen
PY - 2016
Y1 - 2016
N2 - Segmentation of airway trees from CT scans of lungs has important clinical applications, in relation to the diagnosis of chronic obstructive pulmonary disease (COPD). Here we present a method based on multiple hypothesis tracking (MHT) and template matching, originally devised for vessel segmentation, to extract airway trees. Idealized tubular templates are constructed and ranked using scores assigned based on the image data. Several such regularly spaced hypotheses are used in constructing a hypothesis tree, which is then traversed to obtain improvedsegmentation results.
AB - Segmentation of airway trees from CT scans of lungs has important clinical applications, in relation to the diagnosis of chronic obstructive pulmonary disease (COPD). Here we present a method based on multiple hypothesis tracking (MHT) and template matching, originally devised for vessel segmentation, to extract airway trees. Idealized tubular templates are constructed and ranked using scores assigned based on the image data. Several such regularly spaced hypotheses are used in constructing a hypothesis tree, which is then traversed to obtain improvedsegmentation results.
M3 - Poster
Y2 - 31 July 2016 through 6 August 2016
ER -
ID: 165274934