Explicable intensity-aware 3D cerebrovascular segmentation with planar representation.

Abstract

Cerebrovascular segmentation provides valuable cues for cerebrovascular diseases. Deep learning has achieved remarkable success in cerebrovascular segmentation, but relies on colossal computing power. To address existing challenges, we studied the intensity characteristics in cerebrovascular imaging and proposed an explicable intensity-aware cerebrovascular segmentation (EI-Seg) with 3D and tri-planar representations to promote accurate and efficient feature learning. In particular, EI-Seg has sufficient semantic interpretability, guiding the model to generate low-dimensional feature maps. Through the strategies of disentanglement and cycle consistency, EI-Seg can accurately describe the semantic features of cerebrovasculature in the latent space using tri-planes, thereby avoiding many redundant parameters and subspaces. More importantly, the inference phase of the model is only completed under the path of tri-planar representation, guiding the nearly 2D structure to achieve 3D semantic representation, thereby saving a lot of computing power. Experimental results confirm that EI-Seg has practically no performance loss, but its cost efficiency far surpasses other competitors. Our code is available at https://github.com/USTB-MEDAI/EI-Seg.