Effectiveness of Pseudo 3D Feature Learning for Spinal Segmentation by CNN with U-Net Architecture
Effectiveness of Pseudo 3D Feature Learning for Spinal Segmentation by CNN with U-Net Architecture
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DOI：
10.18178/joig.7.3.107-111
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发表时间：
2019
影响因子：
--
通讯作者：
Naofumi Shigeta;Mikoto Kamata;M. Kikuchi
中科院分区：
文献类型：
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作者：
Naofumi Shigeta;Mikoto Kamata;M. Kikuchi
在医学领域，人们一直希望从CT图像中自动提取脊柱区域。在各种图像分割方法中，一种称为U-Net[1]的卷积神经网络模型已被证明在小数据集大小下获得良好的性能。Kamata et al.[2]先前的研究将U-Net应用于脊柱分割任务，对于未学习的CT图像，准确率达到82.7%。然而，该方法在三维形状的精度上存在困难。本研究通过对U-Net进行伪三维特征学习，尝试以更高的精度提取脊柱区域。
—In the medical field, automatic extraction of spinal region from CT images has been desired. Among various methods for image segmentation, one of the convolutional neural network models called U-Net [1] has been shown to attain good performance with small data set size. Previous study by Kamata et al. [2] applied U-Net for spine segmentation task and achieved 82.7% accuracy for unlearned CT images. However, the method had difficulty in the precision of the 3D shape. This study attempted extraction of spine region with higher precision by adopting pseudo 3D feature learning for U-Net.