Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting.

ABSTRACT: For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilis

SUBMITTER: Bouget D 

PROVIDER: S-EPMC9364874 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature