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Early detection of brain metastases (BM) is one of the determining factorsfor the successful treatment of patients with cancer; however, the accuratedetection of small BM lesions (< 15mm) remains a challenging task. Wepreviously described a framework for the detection of small BM insingle-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combinedclassical image processing (IP) with a dedicated convolutional neural network,taking approximately 30 seconds to process each dataset due tocomputation-intensive IP stages. To overcome the speed limitation, this studyaims to reformulate the framework via an augmented pair of CNNs (eliminatingthe IP) to reduce the processing times while preserving the BM detectionperformance. Our previous implementation of the BM detection algorithm utilizedLaplacian of Gaussians (LoG) for the candidate selection portion of thesolution. In this study, we introduce a novel BM candidate detection CNN(cdCNN) to replace this classical IP stage. The network is formulated to have(1) a similar receptive field as the LoG method, and (2) a bias for thedetection of BM lesion loci. The proposed CNN is later augmented with aclassification CNN to perform the BM detection task. The cdCNN achieved 97.4%BM detection sensitivity when producing 60K candidates per 3D MRI dataset,while the LoG achieved 96.5% detection sensitivity with 73K candidates. Theaugmented BM detection framework generated on average 9.20 false-positive BMdetections per patient for 90% sensitivity, which is comparable with ourprevious results. However, it processes each 3D data in 1.9 seconds, presentinga 93.5% reduction in the computation time.

Augmented Networks for Faster Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI