<scp>GBT</scp> ‐ <scp>SAM</scp> : A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on <scp>mp‐MRI</scp>

ABSTRACT Gliomas are aggressive brain tumors that require accurate imaging‐based diagnosis, where automated segmentation plays a central role in assessing tumor morphology and guiding treatment decisions. Manual delineation of gliomas is time‐consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate clinical workload. However, existing methods often fail to fully exploit the information available in multi‐parametric MRI (mp‐MRI), particularly inter‐slice contextual information. In addition, they typically require considerable computational resources and often lack robustness across different tumor types and imaging conditions. We present GBT‐SAM, a parameter‐efficient deep learning framework that adapts the large‐scale Segment Anything Model (SAM) to volumetric mp‐MRI data. GBT‐SAM reduces training complexity by selecting fewer than 2.6% of slices per scan while still incorporating all four MRI modalities, thereby preserving essential tumor‐related information at minimal computational cost. Furthermore, our model is trained using a two‐step fine‐tuning strategy that combines a depth‐aware module to capture inter‐slice correlations with lightweight LoRA‐based adaptation layers, resulting in only 9.97 M trainable parameters, the lowest among existing SAM‐based approaches. GBT‐SAM is trained exclusively on the BraTS Adult Glioma dataset and achieves a Dice score of 92.66. Also, it is evaluated on additional Meningioma, Pediatric Glioma, and Sub‐Saharan Glioma datasets in order to demonstrate its robust generalization performance. These results highlight GBT‐SAM as a computationally efficient and domain‐robust framework for brain tumor segmentation from mp‐MRI. Our code and models are available at https://github.com/vpulab/gbt‐sam .