Background/Objectives:　The　automatic　left　ventricle　segmentation　in　transesophageal　echocardiography　(TEE)　is　of　significant　importance.　In　this　paper,　we　constructed　a　large-scale　TEE　apical　four-chamber　view　(A4CV)　image　dataset　and　proposed　an　automatic　left　ventricular　segmentation　method　for　the　TEE　A4CV　based　on　the　UNeXt　deep　neural　network.　Methods:　UNeXt,　a　variant　of　U-Net　integrating　a　multilayer　perceptron,　was　employed　for　left　ventricle　segmentation　in　the　TEE　A4CV　because　it　could　yield　promising　segmentation　performance　while　reducing　both　the　number　of　network　parameters　and　computational　complexity.　We　also　compared　the　proposed　method　with　U-Net,　TransUNet,　and　Attention　U-Net　models.　Standard　TEE　A4CV　videos　were　collected　from　60　patients　undergoing　cardiac　surgery,　from　the　onset　of　anesthesia　to　the　conclusion　of　the　procedure.　After　preprocessing,　a　dataset　comprising　3000　TEE　images　and　their　corresponding　labels　was　generated.　The　dataset　was　randomly　divided　into　training,　validation,　and　test　sets　in　an　8:1:1　ratio　on　the　patient　level.　The　training　and　validation　sets　were　used　to　train　the　UNeXt,　U-Net,　TransUNet,　and　Attention　U-Net　models　for　left　ventricular　segmentation.　The　dice　similarity　coefficient　(DSC)　and　Intersection　over　Union　(IoU)　were　used　to　evaluate　the　segmentation　performance　of　each　model,　and　the　Kruskal-Wallis　test　was　employed　to　analyze　the　significance　of　DSC　differences.　Results:　On　the　test　set,　the　UNeXt　model　achieved　an　average　DSC　of　88.60%,　outperforming　U-Net　(87.76%),　TransUNet　(85.75%,　p　＜　0.05),　and　Attention　U-Net　(79.98%;　p　＜　0.05).　Additionally,　the　UNeXt　model　had　a　smaller　number　of　parameters　(1.47　million)　and　floating　point　operations　(2.28　giga)　as　well　as　a　shorter　average　inference　time　per　image　(141.73　ms),　compared　to　U-Net　(185.12　ms),　TransUNet　(209.08　ms),　and　Attention　U-Net　(201.13　ms).　The　average　IoU　of　UNeXt　(77.60%)　was　also　higher　than　that　of　U-Net　(76.61%),　TransUNet　(77.35%),　and　Attention　U-Net　(68.86%).　Conclusions:　This　study　pioneered　the　construction　of　a　large-scale　TEE　A4CV　dataset　and　the　application　of　UNeXt　to　left　ventricle　segmentation　in　the　TEE　A4CV.　The　proposed　method　may　be　used　for　automatic　segmentation　of　the　left　ventricle　in　the　TEE　A4CV.