Development and clinical application of automatic reconstruction technology for a three-dimensional visualization model of brain tumors

Abstract:

Objective To study and develop a three-dimensional visualization model that automatically reconstructs common brain tumors and important structures around them based on multimodal magnetic resonance imaging（MRI） data of the head and to validate its performance and clinical applicability.Methods Multimodal MRI data of the head with common brain tumors were collected and divided into training set，validation set，and clinical testing set. In the training and verification sets，the system's ability to automatically segment and reconstruct brain tumors and surrounding structures was trained through the algorithm of 3D depth convolutional neural network. In the clinical testing set，the reconstruction was completed by the system and a human，respectively，and the reconstruction efficiency and image quality were compared between the two methods.Results The time spent on completing the integrated model reconstruction of a tumor and its surrounding structure was significantly reduced from 5442±623 seconds （by a human） to （657±78） seconds（by the system）（t=27.530，P=0.000）. Meanwhile，the model reconstructed by the system had high consistency with the original image（Dice coefficient=0.92），and there was no significant difference in the image quality between the system and human reconstruction.Conclusion The automatic segmentation and fully automatic 3D visualization reconstruction of brain tumors and their surrounding structures using algorithms such as deep learning based on multimodal imaging data are accurate，efficient，and reliable，which is of great significance in the diagnosis of brain tumors and the formulation of surgical plans.