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The motivation of our work is to present a new visualization-guided computingparadigm to combine direct 3D volume processing and volume rendered clues foreffective 3D exploration such as extracting and visualizing microstructuresin-vivo. However, it is still challenging to extract and visualize highfidelity 3D vessel structure due to its high sparseness, noisiness, and complextopology variations. In this paper, we present an end-to-end deep learningmethod, VC-Net, for robust extraction of 3D microvasculature through embeddingthe image composition, generated by maximum intensity projection (MIP), into 3Dvolume image learning to enhance the performance. The core novelty is toautomatically leverage the volume visualization technique (MIP) to enhance the3D data exploration at deep learning level. The MIP embedding features canenhance the local vessel signal and are adaptive to the geometric variabilityand scalability of vessels, which is crucial in microvascular tracking. Amulti-stream convolutional neural network is proposed to learn the 3D volumeand 2D MIP features respectively and then explore their inter-dependencies in ajoint volume-composition embedding space by unprojecting the MIP features into3D volume embedding space. The proposed framework can better capture small /micro vessels and improve vessel connectivity. To our knowledge, this is thefirst deep learning framework to construct a joint convolutional embeddingspace, where the computed vessel probabilities from volume rendering based 2Dprojection and 3D volume can be explored and integrated synergistically.Experimental results are compared with the traditional 3D vessel segmentationmethods and the deep learning state-of-the-art on public and real patient(micro-)cerebrovascular image datasets. Our method demonstrates the potentialin a powerful MR arteriogram and venogram diagnosis of vascular diseases.

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data