A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab

Proper liver segmentation is a key step in many clinical applications, including computer-assisted diagnosis, radiation therapy and volume measurement. However, liver segmentation is still challenging due to fuzzy boundary, complex liver anatomy, present of pathologies, and diversified shape. This paper presents a novel two-stage liver detection and segmentation model DSL. The first stage uses improved Faster Regions with CNN features (Faster R-CNN) to detect approximate position of liver. The obtained images are processed and input into DeepLab to obtain the contour of liver. The proposed approach is validated on two datasets MICCAI-Sliver07 and 3Dircadb. Experimental results reveal that the proposed method outperforms the state-of-the-art solutions in terms of volume overlap error, average surface distance, relative volume difference, and total score.