Research On Key Technologies For Multi-organ Segmentation And Reconstruction Based On CT Medical Imaging

Medical image segmentation and reconstruction are key tasks in medical-aided diag-nosis.Its purpose is to accurately identify target organs and tissues from the pixel level and restore their three-dimensional structures.Continuously improving precision in complex tissue structures has become an urgent need for precision medicine and smart medicine,and its research has certain practical significance.This paper mainly studies the key technologies of multi-organ segmentation and re-construction based on CT medical images,focuses on the lightweight segmentation algo-rithm based on Transformer,the efficient segmentation architecture search strategy based on NAS,and the 3D reconstruction based on active contour model and graph convolution.algorithm.First,in the lightweight segmentation algorithm based on Transformer,a global self-attention approximation scheme with low computational complexity is derived.On the basis of the approximate scheme,a convolution operation and self-attention operation are established.The multi-branch structure is used to reconstruct the Transformer,and the reconstructed Transformer is embedded into an encoder-decoder U-shaped architecture with skip connections to achieve abdominal multi-organ segmentation.The algorithm can use fewer parameters and lower computational costs Get very good segmentation per-formance.Second,efficient segmentation architectures are explored based on Neural Archi-tecture Search（NAS）.A lightweight Transformer is introduced,and a multi-scale search space including various convolution operators and the light Transformer is designed.An efficient resource-constrained search strategy is proposed to train the search network,which efficiently explores the search space and finds the optimal architecture given the segmentation task and computational resources.In addition,in the 3D reconstruction algorithm based on active contour model and graph convolution,an image coding module for processing 3D CT data,a segmentation module for 3D voxel segmentation of CT data,and a module for connecting voxel features and grid features are designed.The feature connection module is used to assist the mesh deformation.In the mesh deformation part,the active contour model is used to optimize the shape regularization term of the graph convolution,so as to ensure the smoothness of the mesh surface and obtain a smooth and accurate mesh surface.In a word,through the exploration of lightweight segmentation algorithms,efficient segmentation architecture search strategies,and 3D reconstruction algorithms,this paper provides solutions for medical image segmentation and reconstruction tasks,which have certain reference significance.