Cooperative-Net: An end-to-end multi-task interaction network for unified reconstruction and segmentation of MR image

Background and objective: In clinical applications, there is an increasing demand for rapid acquisition and automated analysis of magnetic resonance imaging (MRI) data. However, most existing methods focus on either MR image reconstruction from undersampled data or segmentation using fully sampled data, hardly considering MR image segmentation in fast imaging scenarios. Consequently, it is imperative to investigate a multi -task approach that can simultaneously achieve high scanning acceleration and accurate segmentation results. Methods: In this paper, we propose a novel end -to -end multi -task interaction network, termed as the CooperativeNet, which integrates accelerated MR imaging and multi -class tissue segmentation into a unified framework. The Cooperative -Net consists of alternating reconstruction modules and segmentation modules. To facilitate effective interaction between the two tasks, we introduce the spatial -adaptive semantic guidance module, which leverages the semantic map as a structural prior to guide MR image reconstruction. Furthermore, we propose a novel unrolling network with a multi -path shrinkage structure for MR image reconstruction. This network consists of parallel learnable shrinkage paths to handle varying degrees of degradation across different frequency components in the undersampled MR image, effectively improving the quality of the recovered image. Results: We use two publicly available datasets, including the cardiac and knee MR datasets, to validate the efficacy of our proposed Cooperative -Net. Through qualitative and quantitative analysis, we demonstrate that our method outperforms existing state-of-the-art multi -task approaches for joint MR image reconstruction and segmentation. Conclusions: The proposed Cooperative -Net is capable of achieving both high accelerated MR imaging and accurate multi -class tissue segmentation.