Segmentation　of　a　liver　in　computed　tomography　(CT)　images　is　an　important　step　toward　quantitative　biomarkers　for　a　computer-aided　decision　support　system　and　precise　medical　diagnosis.　To　overcome　the　difficulties　that　come　across　the　liver　segmentation　that　are　affected　by　fuzzy　boundaries,　stacked　autoencoder　(SAE)　is　applied　to　learn　the　most　discriminative　features　of　the　liver　among　other　tissues　in　abdominal　images.　In　this　paper,　we　propose　a　patch-based　deep　learning　method　for　the　segmentation　of　a　liver　from　CT　images　using　SAE.　Unlike　the　traditional　machine　learning　methods,　instead　of　anticipating　pixel　by　pixel　learning,　our　algorithm　utilizes　the　patches　to　learn　the　representations　and　identify　the　liver　area.　We　preprocessed　the　whole　dataset　to　get　the　enhanced　images　and　converted　each　image　into　many　overlapping　patches.　These　patches　are　given　as　input　to　SAE　for　unsupervised　feature　learning.　Finally,　the　learned　features　with　labels　of　the　images　are　fine　tuned,　and　the　classification　is　performed　to　develop　the　probability　map　in　a　supervised　way.　Experimental　results　demonstrate　that　our　proposed　algorithm　shows　satisfactory　results　on　test　images.　Our　method　achieved　a　96.47%　dice　similarity　coefficient　(DSC),　which　is　better　than　other　methods　in　the　same　domain.