@inproceedings{
chen2024connectivitybased,
title={Connectivity-based Cerebrovascular Segmentation in Time-of-Flight Magnetic Resonance Angiography},
author={Zan Chen and Xiao Yu and Yuanjing Feng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=gkdqXsTvWO}
}
Accurate segmentation of cerebrovascular structures from TOF-MRA is vital for treating cerebrovascular diseases. However, existing methods rely on voxel categorization, leading to discontinuities in fine vessel locations. We propose a connectivity-based cerebrovascular segmentation method that considers inter-voxel relationships to overcome this limitation. By modeling connectivity, we transform voxel classification into predicting inter-voxel connectivity. Given cerebrovascular structures' sparse and widely distributed nature, we employ sparse 3D Bi-level routing attention to reduce computational overhead while effectively capturing cerebrovascular features. To enhance directional information extraction, we utilize the 3D-direction excitation block. Additionally, the 3D-direction interactive block continuously augments direction information in the feature map and sends it to the skip connection. We compare our method with current state-of-the-art cerebrovascular segmentation techniques and classical medical image segmentation methods using clinical and open cerebrovascular datasets. Our method demonstrates superior performance, outperforming existing approaches. Ablation experiments further validate the effectiveness of our proposed method.
Connectivity-based Cerebrovascular Segmentation in Time-of-Flight Magnetic Resonance Angiography
Zan Chen Xiao Yu Yuanjing Feng