Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

ABSTRACT: Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volum

SUBMITTER: Boaro A 

PROVIDER: S-EPMC9474556 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature