In　recent　years,　cancer　has　seriously　damaged　human　health,　and　the　morphological　structure　of　cells　serves　as　an　important　basis　for　cancer　diagnosis　and　grading.　Automatic　cell　segmentation　based　on　deep　learning　has　become　an　important　means　of　computer-aided　pathological　diagnosis.　Aiming　at　the　existing　problems　of　rough　segmentation　boundaries　and　inaccurate　segmentation　in　cell　image　segmentation,　this　paper　designs　a　cell　image　segmentation　network　model　(ERF-TransUNet)　based　on　edge　feature　residual　fusion　from　the　perspective　of　mutual　complementarity　and　constraint　between　edge　features　and　object　features.　The　model　uses　a　hybrid　architecture　of　CNN　and　Transformer　to　extract　multi-scale　features　from　cell　images,　and　adds　independent　edge　feature　extraction　modules　and　residual　fusion　modules　to　enhance　the　extraction　of　edge　features　and　their　constraints　when　fusing　with　cell　object　features,　improving　the　accuracy　of　cell　contour　positioning.　Through　experiments　on　two　gland　cell　datasets,　CRAG　and　Glas,　and　comparing　the　segmentation　effects　with　current　popular　deep　learning　models,　the　network　model　proposed　in　this　paper　has　achieved　good　performance　in　both　Dice　coefficient　and　Hausdorff　distance,　which　can　effectively　improve　the　segmentation　effect　of　cell　images.