Research On Medical Image Segmentation For Craniotomy Robot Preoperative Planning System

In the preoperative planning system of the craniotomy robot,the analysis of the lesion area and the efficient and accurate segmentation play a vital role in improving the safety and accuracy of the craniotomy robot.This paper analyzes the key technologies of intelligent medical image processing in the preoperative planning system of craniotomy robot,and focuses on in-depth research on the key contents of intelligent MRI image segmentation of brain tumor.The specific work contents are as follows:(1)The development of medical image segmentation methods at home and abroad and their important role and significance in robot preoperative planning system are comprehensively expounded.The deep learning-based brain tumor medical image segmentation algorithm is analyzed,the problems existing in the current methods are put forward,and the research content of this paper is given according to the problems.This paper introduces the basic structure and principle of convolutional neural networks,the theory of convolutional neural networks and the commonly used deep learning framework,summarizes the NMR medical imaging data used in this experiment,introduces the experimental data set used in this paper and the segmentation evaluation indicators of brain tumor MRI images,as well as the meaning and calculation methods of each indicator.(2)Aiming at the low accuracy of brain tumor segmentation,the degradation of the traditional neural network model network,and the gradient disappearance,this paper proposes an intelligent brain tumor segmentation algorithm based on the residual network and the void convolution pooling pyramid.This network integrates the advantages of residual network feature reuse and void convolution pooling pyramid to expand the receptive field and generate multi-scale features,which can adapt to the feature requirements of different levels of networks,solve the problem of network degradation,improve the overall characterization ability of the network,and improve the accuracy and efficiency of brain tumor segmentation.Through the comparative analysis of the experimental results,the segmentation performance of this method is greatly improved compared with other network models.(3)For 2D convolution network on the spatial location information utilization,put forward a based on dense connection and channel and spatial attention 3DMRI brain tumor segmentation network,dense connection network reduce the difficulty of training depth network and improve the transfer efficiency of gradient flow,channel and spatial attention mechanism can guide model to the most useful feature area,improve the model attention to information,so as to better express characteristics.This method,Dice scores of 0.9218,0.9174,0.8755 in WT,TC and ET,also achieved excellent performance in other evaluation indicators and its network performance reached a high level.The brain tumor MRI image segmentation method studied in this paper can provide accurate craniotomy robot preoperative planning system lesion area segmentation results,and provide initial parameters for the robot preoperative planning system,for the surgical area,bone window location,surgical incision and subsequent craniotomy operation plays a vital role,laid a solid foundation for effective and executable surgical plan,can improve the safety and effectiveness of craniotomy robot,has powerful clinical application value.