  Fast and accurate segmentation of neonatal brain images is highly desired to
better understand and detect changes during development and disease. Yet, the
limited availability of ground truth datasets, lack of standardized acquisition
protocols, and wide variations of head positioning pose challenges for method
development. A few automated image analysis pipelines exist for newborn brain
MRI segmentation, but they often rely on time-consuming procedures and require
resampling to a common resolution, subject to loss of information due to
interpolation and down-sampling. Without registration and image resampling,
variations with respect to head positions and voxel resolutions have to be
addressed differently. In deep-learning, external augmentations are
traditionally used to artificially expand the representation of spatial
variability, increasing the training dataset size and robustness. However,
these transformations in the image space still require resampling, reducing
accuracy specifically in the context of label interpolation. We recently
introduced the concept of resolution-independence with the Voxel-size
Independent Neural Network framework, VINN. Here, we extend this concept by
additionally shifting all rigid-transforms into the network architecture with a
four degree of freedom (4-DOF) transform module, enabling resolution-aware
internal augmentations (VINNA). In this work we show that VINNA (i)
significantly outperforms state-of-the-art external augmentation approaches,
(ii) effectively addresses the head variations present specifically in newborn
datasets, and (iii) retains high segmentation accuracy across a range of
resolutions (0.5-1.0 mm). The 4-DOF transform module is a powerful, general
approach to implement spatial augmentation without requiring image or label
interpolation. The specific network application to newborns will be made
publicly available as VINNA4neonates.
