  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 for automatically learning deep
features that enable optimal performance in a NL-PLF framework. The learned
deep features are further utilized in defining a similarity measure for atlas
selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09
for left ventricle segmentation, our approach achieved 0.833 in averaged Dice
metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on
LV-09 dataset, comparing favorably with the other automatic left ventricle
segmentation methods. We also tested our approach on Cardiac Atlas Project
(CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method
achieved 0.815 in ADM, ranking highest at the time of writing.
