A Multi-Stage DCNN Method for Liver Tumor Segmentation

Medical image segmentation is one of the most important tasks in machine learning. In order to help with the doctors, we focus on computer-aided automatic computer tomography (CT) segmentation of liver tumors. Instead of using a single model during the segmentation process, we argue that the filter made by the model of object detection before segmentation could improve the accuracy of segmentation. From the motivation above, we proposed a multi-stage deep convolutional neural network (DCNN) model, which can be used for segmentation of the tumors in the livers. To further improve the accuracy of the object detection model, we proposed an iterative training method which boosts the performance of our model and yield more accurate localization of the tumors. Finally, the evaluation of proposed method on our private dataset from hospital and public dataset from MICCAI-LiTS2017 suggested that our model is superior to the current state-of-the-art models.