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Research on 3D Medical Image Segmentation based on improved 3D-Unet

机译:基于改进的3D-unet的3D医学图像分割研究

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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.
机译:磁共振成像(MRI)图像具有高组织分辨率和富有的对比度,并且是研究海马形态学的重要数据。 海马体积不能达到人脑MRI图像中的理想细分效果,很少有人研究了人脑MRI的三维重建。 本文提出了一种改进的3D-UNET医学图像处理。 通过建立3D分割算法,实现了脑组织的3D重构和可视化,实现了分割算法与3D可视化系统之间的连锁。 实验表明,改进的算法具有高识别率和强大的适应性。

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