Manual delineation of brain metastases for stereotactic radiosurgery (SRS) is time consuming and labor intensive. We successfully constructed a deep learning ensemble, including a single shot detector and U-Net, to detect and subsequently segment brain metastases in MRI for SRS treatment planning. Postcontrast 3D T1-weighted gradient echo MR images from 266 patients were randomly split by 212:54 for model training-validation and testing. For the testing group, an overall sensitivity of 80.4% (189/235 metastases) with 4 false positives per patient, and a median segmentation Dice of 77.9% (61.4% - 86.3%) for the detected metastases were achieved.
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