GPU accelerated segmentation and centerline extraction of tubular structures from medical images
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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).
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.
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.
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.
KeywordsSegmentation Centerline extraction Vessel Airway GPU Parallel
Thanks to the people of the Heterogeneous and Parallel Computing Lab at NTNU for all their assistance and St. Olav’s University Hospital for the datasets. The authors would also like to convey thanks to NTNU and NVIDIA’s CUDA Research Center Program for their hardware contributions to the HPC Lab. Without their continued support, this project would not have been possible.
Conflict of interest
Erik Smistad, Anne C. Elster and Frank Lindseth declare that they have no conflict of interest.
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