Cerebrovascular Segmentation via Vessel Oriented Filtering Network
This work addresses the problem of incomplete and inaccurate segmentation of complex blood vessel networks for medical diagnosis and treatment, representing an incremental improvement by incorporating vessel-specific filters into existing neural network methods.
The paper tackled the challenge of accurate cerebrovascular segmentation from MRA and CTA images by proposing a Vessel Oriented Filtering Network (VOF-Net) that embeds domain knowledge into a CNN, resulting in improved segmentation performance as shown in experiments on CTA and MRA datasets.
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.