Medical Image Segmentation Based on 3D U-net

Silu Chen, Guanghao Hu, Jun Sun
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引用次数: 3

Abstract

For medical image processing, as the target area of the tumor lesions is small, and the boundaries of the organs are blurred, so the segmentation of the medical images is difficult. In the original 3D U-net model, feature extraction is performed on the interest image region by increasing the channel attention mechanism, so that the model keep a watchful eye on key region before segmentation. Test results indicate that the improved model has significantly improved segmentation accuracy relative to the original 3D U-net model and is a valid image segmentation model.
基于三维U-net的医学图像分割
对于医学图像处理而言,由于肿瘤病灶的靶区较小,且器官的边界模糊,因此医学图像的分割比较困难。在原始的3D U-net模型中,通过增加通道关注机制对感兴趣的图像区域进行特征提取,使模型在分割前保持对关键区域的关注。实验结果表明,改进后的模型相对于原来的三维U-net模型有明显的分割精度提高,是一种有效的图像分割模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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