Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method.

ABSTRACT: The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an externa

SUBMITTER: Chen C 

PROVIDER: S-EPMC8401675 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature