Gold　immunochromatographic　assay　(GICA)　is　a　widespread　rapid　detection　method　with　less　cost　but　high　efficiency.　It　is　easy　to　operate　and　dispense　with　professional　staff　and　equipment,　which　conforms　to　the　trend　of　point-of-care　testing　that　advocated　by　modern　medicine.　With　the　development　and　progression　of　medical　detection　technology,　the　qualitative　analysis　that　could　be　easily　performed　with　the　naked　eye　is　not　satisfying　anymore.　In　recent　years,　improving　the　performance　of　quantitative　analysis　of　the　GICA　has　become　a　hot　research　topic.　However,　the　GICA　is　susceptible　to　noise　interference　due　to　various　factors　when　used　in　the　qualitative　analysis　in　clinics.　The　rise　of　artificial　intelligence　has　provided　us　with　new　ideas　and　directions.　As　a　popular　neural　network　in　deep　learning,　convolutional　neural　network　(CNN)　has　achieved　excellent　results　in　image　processing　and　has　been　widely　applied　to　many　fields,　including　biomedical　engineering.　In　this　paper,　CNN　is　applied　to　the　image　segmentation　of　gold　immunochromatographic　strip.　The　grayscale　features　of　the　pre-processed　images　are　learned　by　the　established　CNN　network,　and　then,　the　control　and　test　lines　are　accurately　extracted　and　further　quantitative　analysis　is　performed.　The　results　show　that　the　method　proposed　in　this　paper　has　a　good　segmentation　effect　on　the　GICA,　and　it　also　provides　a　new　scheme　for　the　quantitative　analysis　of　the　GICA.　©　2013　IEEE.