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Updated: Apr 22, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
Parallelized seeded region growing using CUDA
Seongjin Park1, Jeongjin Lee2, Hyunna Lee3
1SW Content Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-Ro, Yuseong-Gu, Daejeon 305-700, Republic of Korea.
Abstract:
This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests.
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