Background　and　objective:　The　effective　segmentation　of　esophageal　squamous　carcinoma　lesions　in　CT　scans　is　significant　for　auxiliary　diagnosis　and　treatment.　However,　accurate　lesion　segmentation　is　still　a　challenging　task　due　to　the　irregular　form　of　the　esophagus　and　small　size,　the　inconsistency　of　spatio-temporal　structure,　and　low　contrast　of　esophagus　and　its　peripheral　tissues　in　medical　images.　The　objective　of　this　study　is　to　improve　the　segmentation　effect　of　esophageal　squamous　cell　carcinoma　lesions.　Methods:　It　is　critical　for　a　segmentation　network　to　effectively　extract　3D　discriminative　features　to　distinguish　esophageal　cancers　from　some　visually　closed　adjacent　esophageal　tissues　and　organs.　In　this　work,　an　efficient　HRU-Net　architecture　(High　-Resolution　U　-Net)　was　exploited　for　esophageal　cancer　and　esophageal　carcinoma　segmentation　in　CT　slices.　Based　on　the　idea　of　localization　first　and　segmentation　later,　the　HRU-Net　locates　the　esophageal　region　before　segmentation.　In　addition,　an　Resolution　Fusion　Module　(RFM)　was　designed　to　integrate　the　information　of　adjacent　resolution　feature　maps　to　obtain　strong　semantic　information,　as　well　as　preserve　the　high　-resolution　features.　Results:　Compared　with　the　other　five　typical　methods,　the　devised　HRU-Net　is　capable　of　generating　superior　segmentation　results.　Conclusions:　Our　proposed　HRU-NET　improves　the　accuracy　of　segmentation　for　squamous　esophageal　cancer.　Compared　to　other　models,　our　model　performs　the　best.　The　designed　method　may　improve　the　efficiency　of　clinical　diagnosis　of　esophageal　squamous　cell　carcinoma　lesions.