  The purpose was to assess the clinical value of a novel DropOut model for
detecting and segmenting brain metastases, in which a neural network is trained
on four distinct MRI sequences using an input dropout layer, thus simulating
the scenario of missing MRI data by training on the full set and all possible
subsets of the input data. This retrospective, multi-center study, evaluated
165 patients with brain metastases. A deep learning based segmentation model
for automatic segmentation of brain metastases, named DropOut, was trained on
multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
The segmentation results were compared with the performance of a
state-of-the-art DeepLabV3 model. The MR sequences in the training set included
pre- and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd
T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D
fluid attenuated inversion recovery (FLAIR), whereas the test set did not
include the IR prepped image-series. The ground truth were established by
experienced neuroradiologists. The results were evaluated using precision,
recall, Dice score, and receiver operating characteristics (ROC) curve
statistics, while the Wilcoxon rank sum test was used to compare the
performance of the two neural networks. The area under the ROC curve (AUC),
averaged across all test cases, was 0.989+-0.029 for the DropOut model and
0.989+-0.023 for the DeepLabV3 model (p=0.62). The DropOut model showed a
significantly higher Dice score compared to the DeepLabV3 model (0.795+-0.105
vs. 0.774+-0.104, p=0.017), and a significantly lower average false positive
rate of 3.6/patient vs. 7.0/patient (p<0.001) using a 10mm3 lesion-size limit.
The DropOut model may facilitate accurate detection and segmentation of brain
metastases on a multi-center basis, even when the test cohort is missing MRI
input data.
