Abstract: Liver segmentation is an important prerequisite for liver disease diagnosis, three-dimensional reconstruction, and surgical planning. To overcome the segmentation challenges caused by complex structure, weak boundary, and large individual differences of liver in abdominal CT volumes, an automatic segmentation method based on multi-view information fusion is proposed. Firstly, a 2D U-shaped convolution network based on atrous spatial pyramid pooling is designed to extract multi-scale features and enlarge the receptive field without increasing the number of network parameters. The proposed 2D U-Net is then applied to slice-wise segmentation from multiple view directions, including axial, sagittal, and coronal planes, thereby compensating for the inability of single-view models to capture inter-slice contextual information. Subsequently, a lightweight 3D convolution network is built to fuse the segmentation results on multiple viewing directions, enabling three-dimensional liver segmentation under limited computational resources and yielding voxel-wise liver probability maps and label assignments for the CT volumes. Finally, the probabilities and labels obtained are used to construct the graph cut energy function to refine the segmentation results, effectively alleviating over-segmentation and under-segmentation. The proposed method can extract 3D features of CT volume indirectly by fusing the segmentation results obtained on different viewing directions and can improve the segmentation accuracy by introducing graph cuts. Experiments are carried out on two public datasets including 3DIRCADb and LiTS, and the Dice obtained by the proposed method on the test set are 0.947 and 0.962 respectively, outperforming those by many existing segmentation methods.

摘要： 肝脏分割是进行肝脏疾病、三维重建和手术规划的重要前提。针对腹部CT序列图像肝脏结构复杂、边界模糊、个体差异大等引起的分割困难，提出一种融合多视图信息的自动分割方法。首先设计基于空洞空间金字塔池化的二维U形网络，在不额外增加模型复杂度的前提下有效捕获多尺度特征并提升感受野范围。然后，将该二维U形网络应用于CT序列横切面、矢状面和冠状面等不同视图方向的二维切片分割，弥补单一视角在建模切片间关联信息方面的不足。随后，构建轻量级的3D卷积网络，将多视图分割结果进行融合，实现资源受限条件下的肝脏三维分割，获取CT序列各像素属于肝脏的概率及标签分配结果。最后，利用已获取的概率和标签构建图割能量函数，对分割结果进行优化，消除过分割与欠分割．提出方法通过融合不同视图的分割结果间接获取CT序列三维特征，并通过结合图割算法提高肝脏分割精度。采用3DIRCADb和LiTS公开数据集进行实验，该方法在测试集上获得的Dice分别为0.947和0.962，优于现有多种分割方法。