Automatic liver tumour segmentation in CT combining FCN and NMF-based deformable model

Abstract

ABSTRACT Automatic liver tumour segmentation is an important step towards digital medical research, clinical diagnosis and therapy planning. However, the existence of noise, low contrast and heterogeneity make the automatic liver tumour segmentation remaining an open challenge. In this work, we focus on a novel automatic method to segment liver tumour in abdomen images from CT scans using fully convolutional networks (FCN) and non-negative matrix factorization (NMF) based deformable model. We train the FCN for semantic liver and tumour segmentation using preprocessed training data by BM3D. The segmented liver and tumour regions of FCN are used as ROI and initialization for the NMF-based tumour refinement, respectively. We refine the surfaces of tumours using a 3D deformable model which derived from NMF and driven by local cumulative spectral histograms (LCSH). The refinement is designed to obtain a smoother, more accurate and natural liver tumour surface. Experiments on 11 clinical datasets demonstrated that the proposed segmentation method achieves satisfactory results.