In recent years, deep learning has made breakthroughs in medical image segmentation, especially the U-Net architecture, which is becoming a benchmark for various medical image segmentation tasks due to the accuracy of its segmentation results.Although U-Net has achieved great success in many medical image segmentation tasks, it is still unsatisfactory in segmenting object boundaries and small objects.This is due to the fact that segmentation networks gradually lose information, especially edge information and small object information, during the process of convolution and downsampling of features.In order to solve the above problems, we design a novel method that uses a multi-scale module as a feature extractor in the contraction path of the Ushaped structure, which better captures the scale changes of the target object by acquiring image features at different scales; in the prediction stage, a contour prediction branch is constructed to constrain the loss of the target's contour, so that the segmentation network pays more attention to the boundaries of the target.We have validated the performance of our method on the Automated Cardiac Diagnosis Challenge (ACDC) and the spleen segmentation tasks of the Medical Segmentation Decathlon (MSD).The results show that our method obtained the best 95% Hausdorff Distance (HD) metrics on both the ACDC dataset and the Spleen dataset, as well as being quite competitive with other state-of-the-art methods in terms of Dice scores.

A model predictive controller was designed in this study for a single supply chain unit. A demand model was described using an autoregressive integrated moving average (ARIMA) model, one that is identified on-line to forecast the future demand. Feedback was used to modify the demand prediction, and profit was chosen as the control objective. To imitate reality, the purchase price was assumed to be a piecewise linear form, whereby the control objective became a nonlinear problem. In addition, a genetic algorithm was introduced to solve the problem. Constraints were put on the predictive inventory to control the inventory fluctuation, that is, the bullwhip effect was controllable. The model predictive control (MPC) method was compared with the order-up-to-level (OUL) method in simulations. The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.