基于3D scSE-UNet的肝脏CT图像半监督学习分割方法

针对分割神经网络需要大量的高质量标签但较难获取的问题，提出基于3D scSE-UNet的半监督学习分割方法. 该方法使用自训练的半监督学习框架，将包含改进的并行空间/特征通道压缩和激励模块（scSE-block+）的3D scSE-UNet作为分割网络. scSE-block+可以从图像空间和特征通道2个方面自动学习图像的有效特征，抑制无用冗余特征，更好地保留图像边缘信息. 在自训练过程中加入全连接条件随机场，对分割网络产生的伪标签进行边缘细化，提升伪标签的精确度. 在LiTS17 Challenge和SLIVER07数据集上验证所提出方法的有效性. 当有标签图像占训练集总图像的30%时，所提方法的Dice相似系数(dice score)为0.941. 结果表明，所提出的半监督学习分割方法可以在仅使用少量标注数据的情况下，取得与全监督分割方法相当的分割效果，有效减轻肝脏CT图像分割对专家标注数据的依赖.

Semi-supervised learning segmentation method of liver CT images based on 3D scSE-UNet

A semi-supervised learning segmentation method based on 3D scSE-UNet was proposed aiming at the problem that segmentation network requires a large number of high-quality labels and it is difficult to obtain. A self-training semi-supervised learning framework is used and 3D scSE-UNet containing the improved concurrent spatial and channel squeeze and excitation module (scSE-block+) in 3D UNet is utilized as the segmentation network. The scSE-block+ can automatically learn effective features of an image from two aspects, image space and feature channel, and suppress redundant features, which helps to preserve more edge information. During the self-training process, dense conditional random field (CRF) is used to refine the segmentation results generated by 3D scSE-UNet, so as to improve the accuracy of the pseudo labels. The effectiveness of the proposed method was verified on LiTS17 Challenge and SLIVER07 dataset. When the labeled images accounted for 30% of the total images in the training set, the dice score of the proposed method was 0.941. Results show that the proposed semi-supervised learning segmentation method can achieve comparable segmentation results with the fully-supervised 3D UNet segmentation method, which effectively reduces the dependence on expert labeled data in liver CT images segmentation.