Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training strategy for automatically learning deep features that enable optimal performance in a NL-PLF framework. Besides, the learned deep features are further utilized in defining a similarity measure for atlas selection. We evaluate our proposed method on two public cardiac MR databases of SATA-13 and LV-09 for left ventricle segmentation, and our learned DFNs with extracted deep features for atlas selection at testing phase achieve state-of-the-art accuracies, e.g., 0.833 in averaged Dice metric (ADM) on SATA-13 database and 0.95 in ADM for epicardium segmentation on LV-09 database. Besides, our method is robust to the cross-database evaluation, e.g., the DFN learned on LV-09 database achieves 0.815 in ADM on SATA-13 database. We also test our proposed method on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieves 0.815 in Dice metric, ranking as the highest result on this dataset.