  The need for fast acquisition and automatic analysis of MRI data is growing
in the age of big data. Although compressed sensing magnetic resonance imaging
(CS-MRI) has been studied to accelerate MRI by reducing k-space measurements,
in current CS-MRI techniques MRI applications such as segmentation are
overlooked when doing image reconstruction. In this paper, we test the utility
of CS-MRI methods in automatic segmentation models and propose a unified deep
neural network architecture called SegNetMRI which we apply to the combined
CS-MRI reconstruction and segmentation problem. SegNetMRI is built upon a MRI
reconstruction network with multiple cascaded blocks each containing an
encoder-decoder unit and a data fidelity unit, and MRI segmentation networks
having the same encoder-decoder structure. The two subnetworks are pre-trained
and fine-tuned with shared reconstruction encoders. The outputs are merged into
the final segmentation. Our experiments show that SegNetMRI can improve both
the reconstruction and segmentation performance when using compressive
measurements.
