# Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI

arXiv: https://arxiv.org/abs/2504.12527 · HTML: https://mlsanity.com/paper/2504.12527/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 18 of 18)

- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models](https://mlsanity.com/paper/2602.15315/) (2026)
- [A methodology for clinically driven interactive segmentation evaluation](https://mlsanity.com/paper/2510.09499/) (2025)
- [Post-Processing Methods for Improving Accuracy in MRI Inpainting](https://mlsanity.com/paper/2510.15282/) (2025)
- [A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development](https://mlsanity.com/paper/2510.20196/) (2025)
- [Instance Awareness of Multi-class Semantic Segmentation Loss Functions](https://mlsanity.com/paper/2604.24276/) (2026)
- [Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation](https://mlsanity.com/paper/2605.31094/) (2026)
- [NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training](https://mlsanity.com/paper/2509.15416/) (2025)
- [Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation](https://mlsanity.com/paper/2510.10679/) (2025)
- [Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models](https://mlsanity.com/paper/2606.18749/) (2026)
- [Beyond scalar losses: calibrating segmentation models via gradient vector field surgery](https://mlsanity.com/paper/2607.14338/) (2026)
- [BrainDiff: Longitudinal Report Generation for Multimodal Brain MRI](https://mlsanity.com/paper/2609.00593/) (2026)
- [Scale-Aware 3D Deep Learning for Robust Brain Metastasis Detection in Multimodal MRI](https://mlsanity.com/paper/2609.10825/) (2026)
- [Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026](https://mlsanity.com/paper/2609.11477/) (2026)
- [Brain Metastases Segmentation for BraTS 2026 Task 1: A Multi-Architecture Comparison](https://mlsanity.com/paper/2609.24769/) (2026)
- [BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation](https://mlsanity.com/paper/2609.30223/) (2026)
- [Sparse cubical complexes for efficient topology-preservation in image data](https://mlsanity.com/paper/2609.37177/) (2026)
- [Dataset-Specific Encoding of Image Content Underlies Cross-Dataset Failure in Brain MRI Foundation Models](https://mlsanity.com/paper/2610.13027/) (2026)

## Abstract (arXiv)

Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis. One approach for improving diagnosis, management, and outcomes is to implement algorithms based on artificial intelligence for the automated segmentation of both pre- and post-treatment MRI brain images. Such algorithms rely on volumetric criteria for lesion identification and treatment response assessment, which are still not available in clinical practice. Therefore, it is critical to establish tools for rapid volumetric segmentations methods that can be translated to clinical practice and that are trained on high quality annotated data. The BraTS-METS 2025 Lighthouse Challenge aims to address this critical need by establishing inter-rater and intra-rater variability in dataset annotation by generating high quality annotated datasets from four individual instances of segmentation by neuroradiologists while being recorded on video (two instances doing "from scratch" and two instances after AI pre-segmentation). This high-quality annotated dataset will be used for testing phase in 2025 Lighthouse challenge and will be publicly released at the completion of the challenge. The 2025 Lighthouse challenge will also release the 2023 and 2024 segmented datasets that were annotated using an established pipeline of pre-segmentation, student annotation, two neuroradiologists checking, and one neuroradiologist finalizing the process. It builds upon its previous edition by including post-treatment cases in the dataset. Using these high-quality annotated datasets, the 2025 Lighthouse challenge plans to test benchmark algorithms for automated segmentation of pre-and post-treatment brain metastases (BM), trained on diverse and multi-institutional datasets of MRI images obtained from patients with brain metastases.