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.　(C)　2018　Elsevier　B.V.　All　rights　reserved.