Fast parallel vessel segmentation. (August 2020)

• Record Type: Journal Article
• Authors: Satpute, Nitin; Naseem, Rabia; Palomar, Rafael; Zachariadis, Orestis; Gómez-Luna, Juan; Cheikh, Faouzi Alaya; Olivares, Joaquín
• Is Part Of: Computer methods and programs in biomedicine. Volume 192(2020)
• Publication Date: 2020-08
• Subjects: Seeded region growing -- GPU -- Kernel termination and relaunch (KTRL) -- Persistent -- Grid-stride loop; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105430
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Parallel implementation of image gradient. GPU acceleration of seeded region growing. Gradient based parallel seeded region growing for vessel segmentation from CT liver images. Evaluation of accurate vessel segmentation shows the speedup of 1.9x compared to the state-of-the-art. Abstract: Background and Objective: Accurate and fast vessel segmentation from liver slices remain challenging and important tasks for clinicians. The algorithms from the literature are slow and less accurate. We propose fast parallel gradient based seeded region growing for vessel segmentation. Seeded region growing is tedious when the inter connectivity between the elements is unavoidable. Parallelizing region growing algorithms are essential towards achieving real time performance for the overall process of accurate vessel segmentation. Methods: The parallel implementation of seeded region growing for vessel segmentation is iterative and hence time consuming process. Seeded region growing is implemented as kernel termination and relaunch on GPU due to its iterative mechanism. The iterative or recursive process in region growing is time consuming due to intermediate memory transfers between CPU and GPU. We propose persistent and grid-stride loop based parallel approach for region growing on GPU. We analyze static region of interest of tiles on GPU for the acceleration of seeded region growing. Results: We aim fast parallel gradient based seeded region growing for vessel segmentation from CT liver slices. The proposed parallel approach is 1.9x faster compared to the state-of-the-art. Conclusion: We discuss gradient based seeded region growing and its parallel implementation on GPU. The proposed parallel seeded region growing is fast compared to kernel termination and relaunch and accurate in comparison to Chan-Vese and Snake model for vessel segmentation.