  Convolutional neural networks have achieved excellent results in automatic
medical image segmentation. In this study, we proposed a novel 3D multi-path
DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four
multi-modal pre-operative MR images. We hypothesized that the multi-path
architecture could achieve more accurate segmentation than a single-path
architecture. 258 GBM patients were included in this study. Each patient had
four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and
FLAIR) and the manually segmented tumor contour. We built a 3D multi-path
DenseNet that could be trained to generate the corresponding GBM tumor contour
from the four MR images. A 3D single-path DenseNet was also built for
comparison. Both DenseNets were based on the encoder-decoder architecture. All
four images were concatenated and fed into a single encoder path in the
single-path DenseNet, while each input image had its own encoder path in the
multi-path DenseNet. The patient cohort was randomly split into a training set
of 180 patients, a validation set of 39 patients, and a testing set of 39
patients. Model performance was evaluated using the Dice similarity coefficient
(DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95%).
Wilcoxon signed-rank tests were conducted to examine the model differences. The
single-path DenseNet achieved a DSC of 0.911$\pm$0.060, ASD of 1.3$\pm$0.7 mm,
and HD95% of 5.2$\pm$7.1 mm, while the multi-path DenseNet achieved a DSC of
0.922$\pm$0.041, ASD of 1.1$\pm$0.5 mm, and HD95% of 3.9$\pm$3.3 mm. The
p-values of all Wilcoxon signed-rank tests were less than 0.05. Both 3D
DenseNets generated GBM tumor contours in good agreement with the manually
segmented contours from multi-modal MR images. The multi-path DenseNet achieved
more accurate tumor segmentation than the single-path DenseNet.

Medical Physics;Image and Video Processing