基于双解码U型卷积神经网络的胰腺分割

计算机断层成像(computed tomography, CT)中, 胰腺分割作为医学图像分析中最具挑战的任务之一, 由于其体积小、形状多变的特点, 导致传统的自动分割方法无法达到理想的分割精度. 利用高级语义特征指导低级特征的思想, 提出一种基于双解码U型卷积神经网络的单阶段胰腺分割模型. 模型由一个编码器和两个解码...

Pancreas Segmentation Based on Dual-decoding U-Net

Pancreas segmentation in computed tomography (CT) is one of the most challenging tasks in medical image analysis. Due to small size and changeable shape, the traditional automatic segmentation methods can not achieve the acceptable segmentation accuracy. By using the idea of high-level semantic features to guide low-level features, this study proposes a single-stage pancreas segmentation model based on dual-decoding U-net. The proposed architecture consists of one encoder and two decoders, which can effectively combine low-level spatial information with high-level semantic information using the features of different coding depths to improve the segmentation accuracy of CT slices without clipping and resolution reduction. The experimental results show that this method can achieve better segmentation performance under full-size input. Moreover, the segmentation result by the proposed method is superior to the single-stage methods on the open dataset for pancreas segementation tasks.