Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography
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arXiv
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| Format: | Preprint |
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2024
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| author | Baur, Kathleen Xiong, Xin Torio, Erickson Du, Rose Juvekar, Parikshit Dorent, Reuben Golby, Alexandra Frisken, Sarah Haouchine, Nazim |
| author_facet | Baur, Kathleen Xiong, Xin Torio, Erickson Du, Rose Juvekar, Parikshit Dorent, Reuben Golby, Alexandra Frisken, Sarah Haouchine, Nazim |
| contents | Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified.The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09636 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography Baur, Kathleen Xiong, Xin Torio, Erickson Du, Rose Juvekar, Parikshit Dorent, Reuben Golby, Alexandra Frisken, Sarah Haouchine, Nazim Image and Video Processing Computer Vision and Pattern Recognition Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified.The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use. |
| title | Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.09636 |