A　new　biomedical　image　segmentation　method　based　on　deep　learning　networks　has　been　constructed.　Under　the　framework　of　deep　learning,　a　dual-branch　deep　learning　network　structure　was　designed　to　perform　average　pooling　and　maximum　pooling　on　input　image　data,　respectively.　The　results　of　pooling　processing　are　sent　to　multi-layer　convolution　for　further　processing,　where　2D　convolution　and　dilated　convolution　are　used,　respectively,　and　the　more　flexible　SoftMax　function　is　selected　for　activation　processing.　Multiple　fully　connected　processes　were　used　between　each　link　and　between　two　branches　to　form　better　fusion　criteria.　The　experimental　results　show　that　our　method　has　a　more　ideal　segmentation　effect　in　both　homologous　and　heterogeneous　training　segmentation　experiments,　with　AC,　SE,　and　SP　indicators　reaching　over　95%,　84%,　and　99%,　respectively.