Automated Brain Metastasis Detection and Segmentation Using Deep-Learning Method

Abstract

<h3>Purpose/Objective(s)</h3> Brain metastases (BM) detection and segmentation can be difficult and time-consuming during stereotactic radiosurgery (SRS) planning. This work aims to develop deep learning models to automatically detect and segment BM on MRI. <h3>Materials/Methods</h3> Two volumetric deep convolutional neural network (V-net) models, general (GM) and small lesion models (SM), were developed using T1-weighted gadolinium contrast enhanced and T2-weighted FLAIR MRIs along with ground truth contours of all BMs collected for 53 patients from our own institution. The GM was developed by transfer learning based on a model previously trained with multi-institutional datasets. The SM was a new model presently trained using images slices with small lesions (volumes < 1 cc). The training sets for both models were the data from 43 patients, a total of 5,204 image sets extracted with three different patch sizes (16 × 16 × 16, 32 × 32 × 32, and 64 × 64 × 64). The obtained models were evaluated using a test set of 10 patients in terms of the Dice similarity coefficient (DSC), sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). <h3>Results</h3> For the GM, the voxel-level segmentation AUCs were 0.90 +/- 0.10, 0.88 +/- 0.10, and 0.87 +/- 0.11 for patch sizes of 16 × 16 × 16, 32 × 32 × 32, and 64 × 64 × 64, respectively, and the detection sensitivity and specificity were 0.86 and 0.94, respectively, for all test datasets (all lesions). For the test data excluding small lesions, the sensitivity and specificity became 0.93 and 0.95, respectively. For SM, a sensitivity of 0.76 and a specificity of 0.91 were achieved for all small lesions in the test datasets. The DSC was 0.92 for GM for all lesions and was 0.82 for SM for all the small BMs. <h3>Conclusion</h3> The two deep learning models, SM and GM, can automatically detect and segment brain metastases with reasonable accuracy, sensitivity, and specificity on the MRIs commonly used for SRS planning. For BMs with volumes > 1 cc, the GM achieved clinically relevant results. With further development using larger datasets, the models can be implemented to improve the clinical practice of SRS planning.