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

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