The　growth　of　healthcare　systems,　particularly　illness　diagnosis　and　treatment　planning,　requires　the　segmentation　of　medical　images.　U-Net　became　the　basic　standard　for　several　medical　image　segmentation　tasks,　with　great　success.　Due　to　the　intrinsic　locality　of　convolutional　processes,　U-Net　frequently　has　constraints　in　explicitly　representing　long-range　dependence.　Transformer,　which　is　built　for　sequence-to-sequence　prediction,　has　emerged　as　a　viable　architecture　with　an　intrinsic　global　self-attention　mechanism,　however　owing　to　insufficient　low-level　information,　it　may　have　restricted　localization　capabilities.　In　this　paper,　Trans-U　is　proposed　as　an　powerful　option　for　medical　image　segmentation　in　this　research　combined　with　Transformer　and　U-Net.　Transformer　utilizes　tokenized　picture　patching　from　convolutional　neural　network　feature　maps　as　input　data　in　order　to　extract　global　context.　To　achieve　exact　localization,　the　decoder　upsamples　the　encoded　features,　which　are　subsequently　integrated　with　the　high-resolution　CNN　feature　maps.　We　claim　that　Transformers　can　be　effective　encoders　for　medical　picture　segmentation　tasks,　especially　when　used　with　U-Net　to　recover　localized　spatial　information　and　improve　finer　details.　Trans-U　outperforms　many　rival　approaches　in　a　variety　of　medical　applications,　such　as　multi-organ　segmentation　and　cardiac　segmentation.　©　2022　IEEE.