Segmenting　the　left　ventricle　from　the　transgastric　short-axis　views　(TSVs)　on　transesophageal　echocardiography　(TEE)　is　the　cornerstone　for　cardiovascular　assessment　during　perioperative　management.　Even　for　seasoned　professionals,　the　procedure　remains　time-consuming　and　experience-dependent.　The　current　study　aims　to　evaluate　the　feasibility　of　deep　learning　for　automatic　segmentation　by　assessing　the　validity　of　different　U-Net　algorithms.　A　large　dataset　containing　1388　TSV　acquisitions　was　retrospectively　collected　from　451　patients　(32%　women,　average　age　53.42　years)　who　underwent　perioperative　TEE　between　July　2015　and　October　2023.　With　image　preprocessing　and　data　augmentation,　3336　images　were　included　in　the　training　set,　138　images　in　the　validation　set,　and　138　images　in　the　test　set.　Four　deep　neural　networks　(U-Net,　Attention　U-Net,　UNet++,　and　UNeXt)　were　employed　for　left　ventricle　segmentation　and　compared　in　terms　of　the　Jaccard　similarity　coefficient　(JSC)　and　Dice　similarity　coefficient　(DSC)　on　the　test　set,　as　well　as　the　number　of　network　parameters,　training　time,　and　inference　time.　The　Attention　U-Net　and　U-Net++　models　performed　better　in　terms　of　JSC　(the　highest　average　JSC:　86.02%)　and　DSC　(the　highest　average　DSC:　92.00%),　the　UNeXt　model　had　the　smallest　network　parameters　(1.47　million),　and　the　U-Net　model　had　the　least　training　time　(6428.65　s)　and　inference　time　for　a　single　image　(101.75　ms).　The　Attention　U-Net　model　outperformed　the　other　three　models　in　challenging　cases,　including　the　impaired　boundary　of　left　ventricle　and　the　artifact　of　the　papillary　muscle.　This　pioneering　exploration　demonstrated　the　feasibility　of　deep　learning　for　the　segmentation　of　the　left　ventricle　from　TSV　on　TEE,　which　will　facilitate　an　accelerated　and　objective　alternative　of　cardiovascular　assessment　for　perioperative　management.