# Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

arXiv: https://arxiv.org/abs/1908.05418 · HTML: https://mlsanity.com/paper/1908.05418/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Deep learning for brain metastasis detection and segmentation in longitudinal MRI data](https://mlsanity.com/paper/2112.11833/) (2021)
- [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022)
- [Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application](https://mlsanity.com/paper/2108.09535/) (2021)
- [Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support](https://mlsanity.com/paper/2501.02346/) (2025)
- [Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation](https://mlsanity.com/paper/2405.10870/) (2024)
- [Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss](https://mlsanity.com/paper/2604.10130/) (2026)

## Abstract (arXiv)

Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases.