总结如下段落：Featuring encoder and decoder structures U-Net and its variants have gained popularity in the field of medical image segmentation However research on model evaluation technology lags behind the

The U-Net and its variants have become popular in medical image segmentation, but there is a lack of research on evaluating these models. Existing techniques are complex, uncertain, and lack comprehensiveness and unity. To address this, the authors propose a method called MIMO that evaluates models in multi-index and multi-organ segmentation tasks. MIMO quantifies segmentation results, accuracy indices, and confidence estimates in a unified metric. The score of MIMO can be used to compare models, with a larger score indicating better performance. Experiments on eight different medical image segmentation models show that MIMO provides novel insights and concise metrics for clinical model deployment. Overall, MIMO is a comprehensive and reliable method that offers concrete information on model performance.