Research On Liver Image Segmentation Method Based On Fusion Of Maskr-cnn And Watershed Algorithm

Liver CT image segmentation is the process of labeling liver regions in a liver CT image dataset.Because CT images are quite different from common images,such as CT images have the characteristics of small contrast gap with nearby organs,generally high noise content,and unconventional states of pathology,it is difficult to segment CT images of liver.Traditional image segmentation methods once played an irreplaceable role in medical image segmentation,but with the rapid development of computer vision and image processing algorithms,more and more neural network algorithms are used in medical image recognition and processing.Among them,the maskr-cnn model optimized on the basis of fasterr-cnn obtains a better image segmentation effect.However,there is still a lot of room for improvement in the performance of the simple maskr-cnn network algorithm structure in segmentation,and the model effect is improved by simply adding a convolutional layer,which will continue to increase the amount of parameters and calculations,which will make training and calculation.becomes difficult.In order to improve the accuracy and effect of maskr-cnn in liver image segmentation,this paper improves the maskr-cnn network according to the characteristics of liver images.The method of non-maximum suppression is used and the calculation method of the loss function is improved.The experimental results show that the improved model not only improves the segmentation results of the model,but also accelerates the convergence speed of the model.However,the improved network still has certain problems,and there are still pixel displacement and over-segmentation problems in the convolution process.In response to this problem,this paper compares three different watershed algorithms,adopts the watershed segmentation method with the best segmentation effect,and fuses the segmentation results with the image after maskr-cnn image segmentation.Through experimental testing and evaluation calculations,the region blocks formed by the distance transform-based watershed algorithm and the advantages of using maskr-cnn to learn image features,the liver image segmentation results are obtained after fusion.The convolutional neural network segmentation algorithm can achieve more accurate liver image segmentation.