  Early detection of brain metastases (BM) is one of the determining factors
for the successful treatment of patients with cancer; however, the accurate
detection of small BM lesions (< 15mm) remains a challenging task. We
previously described a framework for the detection of small BM in
single-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combined
classical image processing (IP) with a dedicated convolutional neural network,
taking approximately 30 seconds to process each dataset due to
computation-intensive IP stages. To overcome the speed limitation, this study
aims to reformulate the framework via an augmented pair of CNNs (eliminating
the IP) to reduce the processing times while preserving the BM detection
performance. Our previous implementation of the BM detection algorithm utilized
Laplacian of Gaussians (LoG) for the candidate selection portion of the
solution. 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 the
detection of BM lesion loci. The proposed CNN is later augmented with a
classification 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. The
augmented BM detection framework generated on average 9.20 false-positive BM
detections per patient for 90% sensitivity, which is comparable with our
previous results. However, it processes each 3D data in 1.9 seconds, presenting
a 93.5% reduction in the computation time.
