Atlas　selection　and　label　fusion　are　two　major　challenges　in　multi-atlas　segmentation.　In　this　paper,we　propose　a　novel　deep　fusion　net　for　better　solving　these　challenges.　Deep　fusion　net　is　a　deep　architecture　by　concatenating　a　feature　extraction　subnet　and　a　non-local　patch-based　label　fusion　(NL-PLF)　subnet　in　a　single　network.　This　network　is　trained　end-to-end　for　automatically　learning　deep　features　achieving　optimal　performance　in　a　NL-PLF　framework.　The　learned　deep　features　are　further　utilized　in　defining　a　similarity　measure　for　atlas　selection.　Experimental　results　on　Cardiac　MR　images　for　left　ventricular　segmentation　demonstrate　that　our　approach　is　effective　both　in　atlas　selection　and　multi-atlas　label　fusion,and　achieves　state　of　the　art　in　performance.　©　Springer　International　Publishing　AG　2016.