Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features

Abstract

Liver tumor segmentation from CT images plays an important role in disease diagnosis and treatment planning. In this paper, we propose an automatic segmentation framework dedicated to accurate liver tumor extraction from CT images. To reduce the segmentation complexity, 3D U-Net is first employed to extract liver region. Then, the liver region is divided into homogeneous superpixels by applying a LI-SLIC based hierarchical iterative segmentation strategy, in which the superpixels are decomposed recursively according to their intensity standard deviation in order to adhere to tumor boundaries precisely. Meanwhile, each pixel in the liver region is roughly classified into tumor or non-tumor by SVM using its local intensity and texture features. Finally, a voting model is developed to identify tumor regions from superpixels based on the pixel-wise classification results. Extensive experiments on two public clinical datasets and comparisons with many state-of-the-art methods demonstrate the superiority of our method on liver tumor segmentation especially for the images with noises, ambiguous boundaries, and low contrast.