  Deep convolutional neural networks (CNNs) have been intensively used for
multi-class segmentation of data from different modalities and achieved
state-of-the-art performances. However, a common problem when dealing with
large, high resolution 3D data is that the volumes input into the deep CNNs has
to be either cropped or downsampled due to limited memory capacity of computing
devices. These operations lead to loss of resolution and increment of class
imbalance in the input data batches, which can downgrade the performances of
segmentation algorithms. Inspired by the architecture of image super-resolution
CNN (SRCNN) and self-normalization network (SNN), we developed a two-stage
modified Unet framework that simultaneously learns to detect a ROI within the
full volume and to classify voxels without losing the original resolution.
Experiments on a variety of multi-modal volumes demonstrated that, when trained
with a simply weighted dice coefficients and our customized learning procedure,
this framework shows better segmentation performances than state-of-the-art
Deep CNNs with advanced similarity metrics.
