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Background  Detecting and segmenting brain metastases is a tedious and time‐consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging.    Purpose  To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep‐learning approach based on a fully convolution neural network (CNN).    Study Type  Retrospective.    Population  In all, 156 patients with brain metastases from several primary cancers were included.    Field Strength  1.5T and 3T. [Correction added on May 24, 2019, after first online publication: In the preceding sentence, the first field strength listed was corrected.]    Sequence  Pretherapy MR images included pre‐ and postgadolinium T 1 ‐weighted 3D fast spin echo (CUBE), postgadolinium T 1 ‐weighted 3D axial IR‐prepped FSPGR (BRAVO), and 3D CUBE fluid attenuated inversion recovery (FLAIR).    Assessment  The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1–3), multiple (4–10), and many (>10) lesions.    Statistical Tests  Network performance was evaluated using precision, recall, Dice/F1 score, and receiver operating characteristic (ROC) curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per‐metastasis basis. The Wilcoxon rank sum test was used to test the differences between patient subgroups.    Results  The area under the ROC curve (AUC), averaged across all patients, was 0.98 ± 0.04. The AUC in the subgroups was 0.99 ± 0.01, 0.97 ± 0.05, and 0.97 ± 0.03 for patients having 1–3, 4–10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice score were 0.79 ± 0.20, 0.53 ± 0.22, and 0.79 ± 0.12, respectively. At the same probability threshold, the network showed an average false‐positive rate of 8.3/patient (no lesion‐size limit) and 3.4/patient (10 mm 3  lesion size limit).    Data Conclusion  A deep‐learning approach using multisequence MRI can automatically detect and segment brain metastases with high accuracy.   Level of Evidence:  3   Technical Efficacy Stage:  2 J. Magn. Reson. Imaging 2020;51:175–182.

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI