Deep Belief Network Modeling for Automatic Liver Segmentation

Abstract

The liver segmentation in CT scan images is a significant step toward the development of a quantitative biomarker for computer-aided diagnosis. In this paper, we propose an automatic feature learning algorithm based on the deep belief network (DBN) for liver segmentation. The proposed method was based on training by a DBN for unsupervised pretraining and supervised fine tuning. The whole method of pretraining and fine tuning is known as DBN-DNN. In traditional machine learning algorithms, the pixel-by-pixel learning is a time-consuming task; therefore, we use blocks as a basic unit for feature learning to identify the liver, which saves memory and computational time. An automatic active contour method is applied to refine the liver in post-processing. The experiments on test images show that the proposed algorithm obtained satisfactory results on healthy and pathological liver CT images. Our algorithm achieved 94.80% Dice similarity coefficient on mixed (healthy and pathological) images while 91.83% on pathological liver images, which is better than those of the state-of-the-art methods.