title: GPU accelerated segmentation and centerline extraction of tubular structures from medical images.
abstract: OBJECTIVE
  To create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs).
  
  
  METHODS
  A cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets.
  
  
  RESULTS
  The implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3-5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods.
  
  
  CONCLUSIONS
  Tubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.
authors.affiliation: Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelandsvei 7-9, NO 7491 , Trondheim, Norway, smistad@idi.ntnu.no.
authors.forename: Erik
authors.initials: E
authors.lastname: Smistad
authors.forename: Anne C
authors.initials: AC
authors.lastname: Elster
authors.forename: Frank
authors.initials: F
authors.lastname: Lindseth
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D011859:Radiography; D025321:Surgery, Computer-Assisted / Q000379:methods*