A method for giant aneurysm segmentation using Euler's elastica. (September 2020)
- Record Type:
- Journal Article
- Title:
- A method for giant aneurysm segmentation using Euler's elastica. (September 2020)
- Main Title:
- A method for giant aneurysm segmentation using Euler's elastica
- Authors:
- Chen, Yu
Courbebaisse, Guy
Yu, Jianjiang
Lu, Dongxiang
Ge, Fei - Abstract:
- Abstract: Computed Tomography Angiography (CTA) is a medical modality having the advantage to reveal anatomical accuracy essential for delivering a precise diagnosis and an appropriate patient's management for treating a cerebral aneurysm. Segmentation of aneurysm shapes through CTA medical images is an important step for geometrical quantification and assessment of rupture risk of cerebral aneurysms. Despite intensive researches in image processing applied to image sequences, aneurysm segmentation remains a major challenge that depends on the geometry and positioning of the aneurysm into the brain. In this paper, the segmentation analysis is performed by using GAC and Euler's elastica models. With Geodesic Active Contour model (GAC) giant aneurysms (Size > 25 mm) are segmented in high-contrast region, while Euler's elastica model estimates the missing boundaries caused for instance by the low contrast of a thrombus (clot within the cavity during endovascular aneurysm repair) with respect to the neighboring tissues. The experiments indicate that our original model is relevant for segmenting the aneurysm cavity and thrombus both with clear and distinct edges well providing a valuable tool for biological mechanisms understanding such as thrombosis into giant aneurysms, and medical validation. Graphical abstract: Highlights: We propose to deal with the aneurysm segmentation by GAC and Euler's elastica model. Our model finds the missing aneurysm boundaries caused by its complexAbstract: Computed Tomography Angiography (CTA) is a medical modality having the advantage to reveal anatomical accuracy essential for delivering a precise diagnosis and an appropriate patient's management for treating a cerebral aneurysm. Segmentation of aneurysm shapes through CTA medical images is an important step for geometrical quantification and assessment of rupture risk of cerebral aneurysms. Despite intensive researches in image processing applied to image sequences, aneurysm segmentation remains a major challenge that depends on the geometry and positioning of the aneurysm into the brain. In this paper, the segmentation analysis is performed by using GAC and Euler's elastica models. With Geodesic Active Contour model (GAC) giant aneurysms (Size > 25 mm) are segmented in high-contrast region, while Euler's elastica model estimates the missing boundaries caused for instance by the low contrast of a thrombus (clot within the cavity during endovascular aneurysm repair) with respect to the neighboring tissues. The experiments indicate that our original model is relevant for segmenting the aneurysm cavity and thrombus both with clear and distinct edges well providing a valuable tool for biological mechanisms understanding such as thrombosis into giant aneurysms, and medical validation. Graphical abstract: Highlights: We propose to deal with the aneurysm segmentation by GAC and Euler's elastica model. Our model finds the missing aneurysm boundaries caused by its complex structure. Our model segments the aneurysm and thrombus with clear and distinct edges very well. Our model provides an invaluable tool for biological mechanisms understanding. … (more)
- Is Part Of:
- Biomedical signal processing and control. Volume 62(2020)
- Journal:
- Biomedical signal processing and control
- Issue:
- Volume 62(2020)
- Issue Display:
- Volume 62, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 62
- Issue:
- 2020
- Issue Sort Value:
- 2020-0062-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-09
- Subjects:
- Giant aneurysm -- Thrombus -- CTA -- Segmentation -- Geodesic active contour -- Euler's elastica model -- Level set method
Signal processing -- Periodicals
Biomedical engineering -- Periodicals
Signal Processing, Computer-Assisted -- Periodicals
Image Processing, Computer-Assisted -- Periodicals
Biomedical Engineering -- Periodicals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/17468094 ↗
http://www.elsevier.com/journals ↗
http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%2329675%232006%23999989998%23626449%23FLA%23&_cdi=29675&_pubType=J&_auth=y&_acct=C000045259&_version=1&_urlVersion=0&_userid=836873&md5=664b5cf9a57fc91971a17faf20c32ec1 ↗ - DOI:
- 10.1016/j.bspc.2020.102111 ↗
- Languages:
- English
- ISSNs:
- 1746-8094
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2087.880400
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14542.xml