基于半监督阶梯网络的肝脏CT 影像分割

针对医学影像数据中标记样本较少且人工标记成本高的问题, 提出一种基于半监督阶梯网络的肝脏CT(Computed Tomography)影像分割模型。首先, 利用超像素分割对数据进行预处理减少数据量, 并以像素点为中心提取子图像(patch)放入半监督模型进行训练。最后, 利用训练模型进行肝脏分割。实验结果表明, 该模型利用少量的标记图片得到了与监督学习相近的结果。

Liver Segmentation in CT Image Based on Semi-Supervised Ladder Network

Aiming at the challenges, such as fewer labeled samples and expensive manual annotation in medical images, a network of liver CT ( Computed Tomography ) images segmentation model based on semi-supervised ladder is presented. First, the input data is reduced by super-pixel segmentation. Next, the patches are extracted around the center of pixels, and the patches are used to train a semi-supervised model. Finally, the trained model is used to achieve liver segmentation. Experiment results show that a small number of labeled pictures are able to obtain similar results with supervised learning.