Central Focused Convolutional Neural Networks: Developing a Data-driven Model for Lung Nodule Segmentation

Lung cancer is the leading cause for cancer related deaths and carrying a dismal prognosis with a 5-year survival rate at only 18%  (Siegel et al., 2016). Treatment therapy monitoring and lung nodule analysis (Aerts et al., 2014) using computed tomography (CT) images are important strategies for early lung cancer diagnosis and survival time improvement. In these approaches, accurate lung nodule segmentation is necessary tha t can directly affect the subsequent analysis results. Specifically, given the fact of growing volumes of clinical imaging data, developing a data-driven segmentation model is of great clinical importance to avoid tedious manual processing and reduce inter-observer variability (Kubota et al., 2011 ).

Source: Medical Image Analysis - June 30, 2017 Category: Radiology Authors: Shuo Wang, Mu Zhou, Zaiyi Liu, Zhenyu Liu, Dongsheng Gu, Yali Zang, Di Dong, Olivier Gevaert, Jie Tian Source Type: research