基于改进U-net网络的2.5D肺实质分割

A 2.5D Lung Segmentation for CT Images Based on Improved U-net

针对胸腔CT影像信息复杂度高、肺器官体积大造成的3D肺实质结构无法快速准确分割的问题,提出了一种基于改进U-net网络的2.5D肺实质分割方法。将3个轴向(冠状面、矢状面、横截面)的胸腔CT影像分别输入改进U-net网络模型进行特征学习,而后对3个轴向的学习结果进行融合,实现3D肺实质分割。使用该方法在中南民族大学认知科学实验室中完成了一系列肺实质分割实验,实验结果表明该方法可以有效地完成3D肺实质分割。

In order to solve the problem that 3D lung parenchyma structure cannot be quickly and accurately segmented due to the high complexity of chest CT image information and large lung organ volume,a 2.5D lung-segmentation method based on improved U-net is proposed.Thoracic CT images in three axial directions(coronal,sagittal,transversal)were input into the improved U-net network model for feature learning,and then the three axial learning results were fused to achieve 3D lung-segmentation.A series of lung parenchyma segmentation experiments have been completed in the Cognitive Science Laboratory of South-central University for Nationalities.The experimental results show that this method can effectively complete 3D lung lung-segmentation.