基于改进3D- unet的三维医学图像分割研究

Hailan Yu, Weili Chen
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引用次数: 1

摘要

磁共振成像(MRI)图像具有高组织分辨率和丰富的对比度,是研究海马形态学的重要数据。海马体积在人脑MRI图像中无法达到理想的分割效果,对人脑MRI三维重建的研究也很少。提出了一种改进的3D-Unet医学图像处理方法。通过建立三维分割算法,实现了脑组织的三维重建和可视化,实现了分割算法与三维可视化系统的联动。实验表明,改进后的算法具有较高的识别率和较强的适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on 3D Medical Image Segmentation based on improved 3D-Unet
Magnetic resonance imaging (MRI) images have high tissue resolution and rich contrast, and are important data for the study of hippocampal morphology. The hippocampal volume can not achieve the ideal segmentation effect in human brain MRI images, and few people have studied the 3D reconstruction of human brain MRI. An improved 3D-Unet medical image processing is proposed in this paper. Through the establishment of 3D segmentation algorithm, the 3D reconstruction and visualization of brain tissue are realized, and the linkage between segmentation algorithm and 3D visualization system is realized. Experiments show that the improved algorithm has high recognition rate and strong adaptability.
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