ROSE: Multi-level super-resolution-oriented semantic embedding for 3D microvasculature segmentation from low-resolution images

Current state-of-the-art segmentation methods often require high-resolution input to attain the high performance, which pushes the limit of data acquisition and brings large computation budgets. Instead, we present an end-to-end deep learning-based method, ROSE, for robust and precise segmentation of high-quality 3D super-resolution (SR) microvasculatures from low-resolution (LR) images as input, which can transform data from the LR imaging domain to the SR semantic domain (cross different modalities and scales). More specifically, a multi-tasking two-stream deep learning framework is proposed to learn the high-fidelity microvasculature SR image and semantic hybrid features simultaneously. During the proposed joint learning process, the high-resolution features of microvasculatures are further enhanced by the learned fine-grained structural/textural features from the microvasculature SR stream with a multi-level embedding scheme through the oriented feature aggregation at different fusion stages. In the constructed joint multi-level hybrid embedding spaces, the instance semantic embedding and the SR imaging embedding can be connected and integrated synergistically. We have conducted extensive experiments using public and real patient micro-cerebrovascular image datasets and compare our framework with traditional 3D vessel segmentation methods and the other state-of-the-art in deep learning. This robust and precise microvascular visualization in different brain regions by our method demonstrates its potential impact in magnetic resonance (MR) angiography and venography for the diagnosis of microvascular disease.