Background/Objectives:　Radial　artery　tracking　(RAT)　in　the　short-axis　view　is　a　pivotal　step　for　ultrasound-guided　radial　artery　catheterization　(RAC),　which　is　widely　employed　in　various　clinical　settings.　To　eliminate　disparities　and　lay　the　foundations　for　automated　procedures,　a　pilot　study　was　conducted　to　explore　the　feasibility　of　U-Net　and　its　variants　in　automatic　RAT.　Methods:　Approved　by　the　institutional　ethics　committee,　patients　as　potential　RAC　candidates　were　enrolled,　and　the　radial　arteries　were　continuously　scanned　by　B-mode　ultrasonography.　All　acquired　videos　were　processed　into　standardized　images,　and　randomly　divided　into　training,　validation,　and　test　sets　in　an　8:1:1　ratio.　Deep　learning　models,　including　U-Net　and　its　variants,　such　as　Attention　U-Net,　UNet++,　Res-UNet,　TransUNet,　and　UNeXt,　were　utilized　for　automatic　RAT.　The　performance　of　the　deep　learning　architectures　was　assessed　using　loss　functions,　dice　similarity　coefficient　(DSC),　and　Jaccard　similarity　coefficient　(JSC).　Performance　differences　were　analyzed　using　the　Kruskal-Wallis　test.　Results:　The　independent　datasets　comprised　7233　images　extracted　from　178　videos　of　135　patients　(53.3%　women;　mean　age:　41.6　years).　Consistent　convergence　of　loss　functions　between　the　training　and　validation　sets　was　achieved　for　all　models　except　Attention　U-Net.　Res-UNet　emerged　as　the　optimal　architecture　in　terms　of　DSC　and　JSC　(93.14%　and　87.93%),　indicating　a　significant　improvement　compared　to　U-Net　(91.79%　vs.　86.19%,　p　＜　0.05)　and　Attention　U-Net　(91.20%　vs.　85.02%,　p　＜　0.05).　Conclusions:　This　pilot　study　validates　the　feasibility　of　U-Net　and　its　variants　in　automatic　RAT,　highlighting　the　predominant　performance　of　Res-UNet　among　the　evaluated　architectures.