Automatic Liver Tumor Segmentation Based on Cascaded Dense-UNet and Graph Cuts

Liver tumor segmentation from abdominal CT image is an important prerequisite for liver disease diagnosis, surgical planning, and radiation therapy. However, the segmentation remains a challenging problem since the tumors in CT images generally have heterogeneous intensities, complicated textures, and ambiguous boundaries. To address this, an automatic, accurate, and robust segmentation method is proposed based on cascaded Dense-Unet and graph cuts. Firstly, the cascaded Dense-UNet is used to obtain liver tumor initial segmentation results as well as the tumor Regions Of Interest (ROIs). Then, an intensity model and a probability model are established respectively by utilizing pixel-wise and patch-wise features in order to distinguish between tumor and non-tumor, and these models are further integrated into the graph cuts energy function to segment the tumor from ROIs accurately. Finally, experiments are carried out on LiTS and 3Dircadb datasets, which are respectively used as training set and testing set, and this method is compared with many other existing automatic segmentation methods. Results demonstrate that the proposed method can segment liver tumors in CT images with different intensity, texture, shape and size more effectively and can extract the tumor boundaries more accurately than other methods, especially for the tumors with low contrasts and ambiguous boundaries.