With　the　rapid　development　of　network　structure,　convolutional　neural　networks　(CNN)　consolidated　its　position　as　a　leading　machine　learning　tool　in　the　field　of　image　analysis.　Therefore,　semantic　segmentation　based　on　CNN　has　also　become　a　key　high-level　task　in　medical　image　understanding.　This　paper　reviews　the　research　progress　on　CNN-based　semantic　segmentation　in　the　field　of　medical　image.　A　variety　of　classical　semantic　segmentation　methods　are　reviewed,　whose　contributions　and　significance　are　highlighted.　On　this　basis,　their　applications　in　the　segmentation　of　some　major　physiological　and　pathological　anatomical　structures　are　further　summarized　and　discussed.　Finally,　the　open　challenges　and　potential　development　direction　of　semantic　segmentation　based　on　CNN　in　the　area　of　medical　image　are　discussed.　Copyright　©　2020　by　Editorial　Office　of　Journal　of　Biomedical　Engineering.