Deep learning technology has been widely applied to the field of medical image segmentation； however， accurate segmentation of bladder images remains challenging. To address the segmentation of bladder magnetic resonance imaging （MRI） iamges， this study proposes MIV-Net， an improved algorithm based on feature fusion and attention mechanisms. The network introduces a multi-scale core feature fusionizer （MCF） to aggregate feature information at different scales and enhance the representation of local structures and boundary details. A vision Transformer （VIT） network is incorporated to capture long-range dependencies， thereby improving the modeling of global contextual information. In the decoding stage， a multilevel attention fusion （MAF） module is employed to integrate multi-level semantic and spatial information， allowing the network to make better use of contextual cues and recover fine segmentation details. Experiments are conducted on two tasks： Bladder wall segmentation and bladder tumor segmentation. For bladder wall segmentation， MIV-Net achieves IoU， Precision， and Dice coefficient values of 83.16%， 91.66%， and 90.81%， respectively. For bladder tumor segmentation， the corresponding values are 82.49%， 90.50%， and 90.40%. These results demonstrate that MIV-Net outperforms current mainstream methods across the key evaluation metrics and provides accurate segmentation of both bladder walls and tumors. By jointly exploiting multi-scale local features， global dependencies， and multi-level contextual information， the proposed model offers a promising technical approach for automated bladder MRI image analysis and may support more accurate computer-aided diagnosis of bladder cancer.Highlights:1. Propose MIV-Net by integrating the multi-scale core feature fusionizer （MCF）， vision Transformer （VIT） network， and multi-level attention fusion （MAF） module for bladder MRI image segmentation.2. Enhance multi-scale feature extraction， global information modeling， and contextual information fusion through the coordinated use of MCF， VIT， and MAF.3. Achieve Dice scores of 90.81% and 90.40% for bladder wall and bladder tumor segmentations， respectively， outperforming current mainstream methods.