Brain Blood Vessel Segmentation based on Region Growing and U-net Neural Network

DOI: https://doi.org/10.62517/jmhs.202405212

Author(s)

Lingsong Huang*, Haoquan Wang

Affiliation(s)

School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi, China *Corresponding Author.

Abstract

Aiming at the problems of insufficient feature extraction in existing deep learning-based cerebrovascular segmentation methods and uneven segmentation results when the original imaging quality is poor, a cerebrovascular segmentation method that integrates region growing and an improved U-net neural network is proposed. Based on the deep learning segmentation results, automatic seed point selection for region growing is performed and fused to improve the segmentation accuracy at the pixel level. In this work, the integrated segmentation method improves the Dice coefficient from 0.805 to 0.878 and the Average Hausdorff Distance (AHD) from 3.949 to 0.903 compared to the deep learning segmentation method.

Keywords

Cerebrovascular Segmentation; U-net Neural Network; Region Growing; Fusion Method

References

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