An Automated Lung Nodule Segmentation Method Based On Nodule Detection Network and Region Growing

Abstract

Segmentation of a specific organ or tissue plays an important role in medical image analysis with the rapid development of clinical decision support systems. With medical imaging equipments, segmenting the lung nodules in the images is able to help physicians diagnose lung cancer diseases and formulate proper schemes. Therefore the research of lung nodule segmentation has attracted a lot of attention these years. However, this task faces some challenges, including the intensity similarity between lung nodules and vessel, inaccurate boundaries and presence of noise in most of the images. In this paper, an automated segmentation method is proposed for lung nodules in CT images. At the first stage, a nodule detection network is used to generate region proposals and locate the bounding boxes of nodules, which are employed as the initial input for the following segmentation. Then the nodules are segmented in the bounding boxes at the second stage. Since the image scale for region growing is reduced by locating the nodule in advance, the efficiency of segmentation can be improved. And due to the localization of nodule before segmentation, some tissues with similar intensity can be excluded from the object region. The proposed method is evaluated on a public lung nodule dataset, and the experimental results indicate the effectiveness and efficiency of the proposed method.