Cerebrovascular Segmentation via Vessel Oriented Filtering Network
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866909397750382592 |
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| author | Guo, Zhanqiang Luan, Yao Feng, Jianjiang Lu, Wangsheng Yin, Yin Yang, Guangming Zhou, Jie |
| author_facet | Guo, Zhanqiang Luan, Yao Feng, Jianjiang Lu, Wangsheng Yin, Yin Yang, Guangming Zhou, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_08868 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cerebrovascular Segmentation via Vessel Oriented Filtering Network Guo, Zhanqiang Luan, Yao Feng, Jianjiang Lu, Wangsheng Yin, Yin Yang, Guangming Zhou, Jie Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Cerebrovascular Segmentation via Vessel Oriented Filtering Network |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2210.08868 |