title: Segmentation of 3D vasculatures for interventional radiology simulation.
abstract: Training in interventional radiology is slowly shifting towards simulation which allows the repetition of many interventions without putting the patient at risk. Accurate segmentation of anatomical structures is a prerequisite of realistic surgical simulation. Therefore, our aim is to develop a generic approach to provide fast and precise segmentation of various virtual anatomies covering a wide range of pathology, directly from patient CT/MRA images. This paper presents a segmentation framework including two segmentation methods: region model based level set segmentation and hierarchical segmentation. We compare them to an open source application ITK-SNAP which provides similar approaches. The subjective human influence such as inconsistent inter-observer errors and aliasing artifacts etc. are analysed. The proposed segmentation techniques have been successfully applied to create a database of various anatomies with different pathologies, which is used in computer-based simulation for interventional radiology training.
authors.affiliation: School of Computing, University of Leeds, UK. y.song@leeds.ac.uk
authors.forename: Yi
authors.initials: Y
authors.lastname: Song
authors.forename: Vincent
authors.initials: V
authors.lastname: Luboz
authors.forename: Nizar
authors.initials: N
authors.lastname: Din
authors.forename: Daniel
authors.initials: D
authors.lastname: King
authors.forename: Derek
authors.initials: D
authors.lastname: Gould
authors.forename: Fernando
authors.initials: F
authors.lastname: Bello
authors.forename: Andy
authors.initials: A
authors.lastname: Bulpitt
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001185:Artificial Intelligence; D001808:Blood Vessels / Q000033:anatomy & histology*; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015641:Radiography, Interventional / Q000379:methods*