Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy

Brain metastases (BMs) are the most common intracranial tumors and stereotactic radiotherapy improved the life quality of patient with BMs, while it requires more time and experience to delineate BMs precisely by oncologists. Deep Learning techniques showed promising applications in radiation oncology. Therefore, we proposed a deep learning-based automatic segmentation of primary tumor volumes for BMs in this work. Magnetic resonance imaging (MRI) of 158 eligible patients with BMs was retrospectively collected in the study. An automatic segmentation model called BUC-Net based on U-Net with cascade strategy and bottleneck module was proposed for auto-segmentation of BMs. The proposed model was evaluated using geometric metrics (Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and Average surface distance (ASD)) for the performance of automatic segmentation, and Precision recall (PR) and Receiver operating characteristic (ROC) curve for the performance of automatic detection, and relative volume difference (RVD) for evaluation. Compared with U-Net and U-Net Cascade, the BUC-Net achieved the average DSC of 0.912 and 0.797, HD95 of 0.901 mm and 0.922 mm, ASD of 0.332 mm and 0.210 mm for the evaluation of automatic segmentation in binary classification and multiple classification, respectively. The average Area Under Curve (AUC) of 0.934 and 0.835 for (Precision-Recall) PR and Receiver Operating Characteristic (ROC) curve for the tumor detection. It also performed the minimum RVD with various diameter ranges in the clinical evaluation. The BUC-Net can achieve the segmentation and modification of BMs for one patient within 10 min, instead of 3-6 h by the conventional manual modification, which is conspicuous to improve the efficiency and accuracy of radiation therapy.