Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Radiosurgery

Background:

• Brain metastases (BMs) detection and segmentation are crucial for patient management.
• Increasing BM prevalence necessitates automated solutions for efficiency and safety.

Purpose of the Study:

• To review and analyze deep learning (DL) auto-segmentation strategies for brain metastases.
• To characterize data used in DL models and assess the performance of current methodologies.
• To discuss challenges and implementation insights in BM segmentation.

Main Methods:

• Literature review of recent advancements in deep learning for medical image segmentation.
• Analysis of auto-segmentation strategies, datasets, and performance metrics for BM detection.
• Evaluation of clinical implementation experiences and challenges.

Main Results:

• Deep learning models have achieved state-of-the-art results in medical image segmentation.
• Automated segmentation significantly reduces manual workload and improves workflow efficiency.
• Various DL strategies show promise for accurate and reliable BM segmentation.

Conclusions:

• Deep learning offers powerful tools for automated brain metastasis segmentation.
• Addressing current challenges can further enhance the clinical utility of these methods.
• Optimized segmentation improves treatment planning and patient outcomes in stereotactic radiosurgery.