  Brain metastasis segmentation poses a significant challenge in medical
imaging due to the complex presentation and variability in size and location of
metastases. In this study, we first investigate the impact of different imaging
modalities on segmentation performance using a 3D U-Net. Through a
comprehensive analysis, we determine that combining all available modalities
does not necessarily enhance performance. Instead, the combination of
T1-weighted with contrast enhancement (T1c), T1-weighted (T1), and FLAIR
modalities yields superior results. Building on these findings, we propose a
two-stage detection and segmentation model specifically designed to accurately
segment brain metastases. Our approach demonstrates that leveraging three key
modalities (T1c, T1, and FLAIR) achieves significantly higher accuracy compared
to single-pass deep learning models. This targeted combination allows for
precise segmentation, capturing even small metastases that other models often
miss. Our model sets a new benchmark in brain metastasis segmentation,
highlighting the importance of strategic modality selection and multi-stage
processing in medical imaging. Our implementation is freely accessible to the
research community on \href{https://github.com/xmindflow/Met-Seg}{GitHub}.
