  The volume estimation of brain regions from MRI data is a key problem in many
clinical applications, where the acquisition of data at high spatial resolution
is desirable. While parallel MRI and constrained image reconstruction
algorithms can accelerate the scans, image reconstruction artifacts are
inevitable, especially at high acceleration factors. We introduce a novel image
domain deep-learning framework for calibrationless parallel MRI reconstruction,
coupled with a segmentation network to improve image quality and to reduce the
vulnerability of current segmentation algorithms to image artifacts resulting
from acceleration. The combination of the proposed image domain deep
calibrationless approach with the segmentation algorithm offers improved image
quality, while increasing the accuracy of the segmentations. The novel
architecture with an encoder shared between the reconstruction and segmentation
tasks is seen to reduce the need for segmented training datasets. In
particular, the proposed few-shot training strategy requires only 10% of
segmented datasets to offer good performance.
