Segmentation of separated Lumens in 3D CTA images of Aortic Dissection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911039365316608 |
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| author | Lohou, Christophe Miguel, Bruno |
| author_facet | Lohou, Christophe Miguel, Bruno |
| contents | Aortic dissection is a serious pathology and requires an emergency management. It is characterized by one or more tears of the intimal wall of the normal blood duct of the aorta (true lumen); the blood under pressure then creates a second blood lumen (false lumen) in the media tissue. The two lumens are separated by an intimal wall, called flap. From the segmentation of connected lumens (more precisely, blood inside lumens) of an aortic dissection 3D Computed Tomography Angiography (CTA) image, our previous studies allow us to retrieve the intimal flap by using Mathematical Morphology operators, and characterize intimal tears by 3d thin surfaces that fill them, these surfaces are obtained by operating the Aktouf et al. closing algorithm proposed in the framework of Digital Topology. Indeed, intimal tears are 3D holes in the intimal flap; although it is impossible to directly segment such non-concrete data, it is nevertheless possible to "materialize" them with these 3D filling surfaces that may be quantified or make easier the visualization of these holes.
In this paper, we use these surfaces that fill tears to cut connections between lumens in order to separate them.
This is the first time that surfaces filling tears are used as an image processing operator (to disconnect several parts of a 3D object). This lumen separation allows us to provide one of the first cartographies of an aortic dissection, that may better visually assist physicians during their diagnosis.
Our method is able to disconnect lumens, that may also lead to enhance several current investigations (registration, segmentation, hemodynamics). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_03655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmentation of separated Lumens in 3D CTA images of Aortic Dissection Lohou, Christophe Miguel, Bruno Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods Aortic dissection is a serious pathology and requires an emergency management. It is characterized by one or more tears of the intimal wall of the normal blood duct of the aorta (true lumen); the blood under pressure then creates a second blood lumen (false lumen) in the media tissue. The two lumens are separated by an intimal wall, called flap. From the segmentation of connected lumens (more precisely, blood inside lumens) of an aortic dissection 3D Computed Tomography Angiography (CTA) image, our previous studies allow us to retrieve the intimal flap by using Mathematical Morphology operators, and characterize intimal tears by 3d thin surfaces that fill them, these surfaces are obtained by operating the Aktouf et al. closing algorithm proposed in the framework of Digital Topology. Indeed, intimal tears are 3D holes in the intimal flap; although it is impossible to directly segment such non-concrete data, it is nevertheless possible to "materialize" them with these 3D filling surfaces that may be quantified or make easier the visualization of these holes. In this paper, we use these surfaces that fill tears to cut connections between lumens in order to separate them. This is the first time that surfaces filling tears are used as an image processing operator (to disconnect several parts of a 3D object). This lumen separation allows us to provide one of the first cartographies of an aortic dissection, that may better visually assist physicians during their diagnosis. Our method is able to disconnect lumens, that may also lead to enhance several current investigations (registration, segmentation, hemodynamics). |
| title | Segmentation of separated Lumens in 3D CTA images of Aortic Dissection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2507.03655 |