Deep Unfolding Segmentation Network for Under-Sampled Magnetic Resonance Images

Magnetic Resonance (MR) image segmentation is a critical task in assisting disease diagnosis. Most existing methods assume that the images being segmented are fully-sampled. However, they ignore the fact that MR images obtained in clinics are often reconstructed from under-sampled k-space data. There are artifacts or distorted details in the reconstruction, leading to unsatisfactory segmentation performance. In this paper, we propose an end-to-end deep unfolding framework to segment desired lesions or organs from the under-sampled k-space data. Specifically, we build a new model to combine the compressive sensing-based under-sampled image reconstruction and level-set-based segmentation. In this model, we introduce an L0 norm on the reconstruction images to enforce smoothing while preserving important edge and boundary, boosting downstream segmentation performance. We employ the Augmented Lagrangian Method to seek the solution and unfold the iterative algorithm into a deep neural network, called deep unfolding segmentation network (DUSNet). To further enhance segmentation performance, we introduce a boundary loss function, which encourages the model to effectively capture edge details of the regions of interest and imposes geometric constraints on the segmentation results. Through end-to-end training, DUSNet can efficiently segment target regions from under-sampled k-space data. Comprehensive experiments demonstrate that the proposed DUSNet outperforms existing state-of-the-art methods for under-sampled MR image segmentation, achieving superior segmentation accuracy.