Automatic liver and tumor segmentation based on deep learning and globally optimized refinement

Automatic segmentation of the liver and hepatic lesions from abdominal 3D computed tomography (CT) images is fundamental tasks in computer-assisted liver surgery planning. However, due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver, accurate liver segmentation and tumor detection are still challenging problems. To address these difficulties, we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized refinement. Firstly, a deep U-net architecture with dense connections is trained to learn the probability map of the liver. Then the probability map goes into the following refinement step as the initial surface and prior shape. The segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of liver. In order to reduce the influence of the surrounding tissues with the similar intensity and texture behavior with the tumor region, during the training procedure, I × liver_label is the input of the network for the segmentation of liver tumor. By doing this, the accuracy of segmentation can be improved. The proposed method is fully automatic without any user interaction. Both qualitative and quantitative results reveal that the proposed approach is efficient and accurate for liver volume estimation in clinical application. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.