  Precise 3D segmentation of infant brain tissues is an essential step towards
comprehensive volumetric studies and quantitative analysis of early brain
developement. However, computing such segmentations is very challenging,
especially for 6-month infant brain, due to the poor image quality, among other
difficulties inherent to infant brain MRI, e.g., the isointense contrast
between white and gray matter and the severe partial volume effect due to small
brain sizes. This study investigates the problem with an ensemble of semi-dense
fully convolutional neural networks (CNNs), which employs T1-weighted and
T2-weighted MR images as input. We demonstrate that the ensemble agreement is
highly correlated with the segmentation errors. Therefore, our method provides
measures that can guide local user corrections. To the best of our knowledge,
this work is the first ensemble of 3D CNNs for suggesting annotations within
images. Furthermore, inspired by the very recent success of dense networks, we
propose a novel architecture, SemiDenseNet, which connects all convolutional
layers directly to the end of the network. Our architecture allows the
efficient propagation of gradients during training, while limiting the number
of parameters, requiring one order of magnitude less parameters than popular
medical image segmentation networks such as 3D U-Net. Another contribution of
our work is the study of the impact that early or late fusions of multiple
image modalities might have on the performances of deep architectures. We
report evaluations of our method on the public data of the MICCAI iSEG-2017
Challenge on 6-month infant brain MRI segmentation, and show very competitive
results among 21 teams, ranking first or second in most metrics.
