Small Lesions, Big Impact: An Automated Segmentation Framework for Brain Metastases*

Accurate delineation of brain metastases on MRI is crucial for effective radiation treatment planning and outcome evaluation. However, manual segmentation of these images is resource-intensive and time-consuming, especially for patients with multiple brain metastases. Recently, several deep learning-based approaches have been introduced for the segmentation of brain tumors; however, these approaches generally fail to delineate small lesions accurately. In this paper, we have introduced a deep learning framework for automatic segmentation of brain metastases with a focus on improving the accuracy for smaller lesions. Our approach utilizes the strengths of Swin Transformer's attention mechanism with a hierarchical encoder to capture the intricate characteristics of brain metastases on multi-modal MRI precisely. In the context of stereotactic radiotherapy, this precision-focused strategy holds particular significance, where accurate segmentation is a necessity even for tiny tumors. The framework was trained and validated on the BraTS-METS dataset, where it achieved a Dice score and volume estimation error of 84.4 ± 3.4 and 0.37 ± 0.4 cc, respectively, on an independent test set. The performance of the framework was compared with two state-of-the-art segmentation models over different tumor size categories, where it demonstrated a considerable improvement in all experiments. Beyond its technical accomplishments, this study is a step forward towards automating stereotactic radiotherapy planning and outcome evaluation for brain tumors.