Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.

ABSTRACT:

Background

Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.

Methods

A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at 5 centers. Five radiology residents and 5 attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) a

SUBMITTER: Luo X 

PROVIDER: S-EPMC11639187 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature