ArXiv Preprint
Accurate cerebrovascular segmentation from Magnetic Resonance Angiography
(MRA) and Computed Tomography Angiography (CTA) is of great significance in
diagnosis and treatment of cerebrovascular pathology. Due to the complexity and
topology variability of blood vessels, complete and accurate segmentation of
vascular network is still a challenge. In this paper, we proposed a Vessel
Oriented Filtering Network (VOF-Net) which embeds domain knowledge into the
convolutional neural network. We design oriented filters for blood vessels
according to vessel orientation field, which is obtained by orientation
estimation network. Features extracted by oriented filtering are injected into
segmentation network, so as to make use of the prior information that the blood
vessels are slender and curved tubular structure. Experimental results on
datasets of CTA and MRA show that the proposed method is effective for vessel
segmentation, and embedding the specific vascular filter improves the
segmentation performance.
Zhanqiang Guo, Yao Luan, Jianjiang Feng, Wangsheng Lu, Yin Yin, Guangming Yang, Jie Zhou
2022-10-17
