  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.

Computer Vision and Pattern Recognition