Detection and segmentation of brain metastases on MRI using 3D-MedDCNet.

ABSTRACT:

Background

Brain metastases are a prevalent and serious complication in cancer patients. For effective treatment planning, precise segmentation is required. While conventional standard neural networks have improved automation, they struggle to detect small metastases without increasing false positives. Addressing this challenge is critical for enhancing clinical outcomes in radiotherapy.

Purpose

Accurate segmentation of brain metastases in magnetic resonance imaging (MRI) is crucial for clinical decision-making and treatment planning. Existing deep learning methods, such as nnUNet, have limited power in sensitively detecting small metastatic lesions without increasing false positives. This study proposes a novel deep learning model aimed at addressing this challenge.

Meth

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12685438 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature