Abstract: In order to solve the problem that the segmentation accuracy of the existing MRI neural network is reduced due to the diversity of input image information.An MRI image segmentation method using Faster R-CNN and U-net mechanism is proposed.Selecting the public cardiac MRI segmentation challenge datasets ACDC and SCD,cleaning and modifing the format of the dataset and sending them to the subsequent neural network.First,Faster R-CNN is applied to target image detection to preprocess the original input image and remove redundant background information.Second,performing U-net segmentation on the preprocessed images.At the same time,in order to test whether the performance and accuracy of the segmentation network are improved after the introduction of Faster R-CNN,ablation experiments and comparison experiments are conducted.In the ablation experiment,the detection and cropping module in the U-net segmentation network is removed,and the U-net and its improved network are selected to do a set of ablation experiments respectively.Experiments show that the average intersection ratio and Dice coefficient of the new method is 0.89 and 0.94 on the ACDC dataset,respectively,which is 7.3% and 5% higher.On the SCD dataset,it is 0.96 and 0.98,which is 5% and 3% higher,respectively.Automatic preprocessing and segmentation of MRI images is achieved.

Key words: U-net, Faster R-CNN, MRI, Segmentation algorithm, Deep learning