Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.
Journal:
BMC medical imaging
PMID:
40140740
Abstract
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI.