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

• Published in: Journal of magnetic resonance imaging Vol. 51; no. 1; pp. 175 - 182
• Main Authors: Grøvik, Endre, Yi, Darvin, Iv, Michael, Tong, Elizabeth, Rubin, Daniel, Zaharchuk, Greg
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2020; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Artificial neural networks; Automation; Brain; Brain - diagnostic imaging; Brain cancer; brain metastases; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Convolution; Deep Learning; Female; Field strength; Ground truth; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Metastases; Metastasis; Middle Aged; multisequence; Neural networks; Neuroimaging; Patients; Performance evaluation; Population studies; Recall; Retrospective Studies; segmentation; Sensitivity and Specificity; Statistical analysis; Statistical tests; Subgroups; Toxicity; deep learning; segmentation; multisequence; brain metastases
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26766

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 T1‐weighted 3D fast spin echo (CUBE), postgadolinium T1‐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 mm3 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.