Literature DB >> 24077409

Segmentation of the thrombus of giant intracranial aneurysms from CT angiography scans with lattice Boltzmann method.

Yu Chen1, Laurent Navarro, Yan Wang, Guy Courbebaisse.   

Abstract

Computed Tomography Angiography (CTA) plays an essential role in the diagnosis, treatment evaluation, and monitoring of cerebral aneurysms. Segmentation of CTA medical images of giant intracranial aneurysms (GIA) provides quantitative measurements of thrombus and aneurysms geometrical characteristics allowing 3D reconstruction. In fact, GIA demonstrated neuroradiological features and propensity of partial or total spontaneous intra-aneurysmal thrombosis generating a thrombus. Despite intensive researches on medical image segmentation, aneurysm (Lumen, Thrombus, and Parent Blood Vessels) segmentation remains as a difficult problem that has not been yet resolved. In this paper, we proposed a Lattice Boltzmann Geodesic Active Contour Method (LBGM) for aneurysm segmentation in CTA images in order to estimate both the volumes of the thrombus and the aneurysm. Although the noise in the CTA images is very strong and the edges of the thrombus are not so different than the surrounding tissues, the aneurysms are segmented effectively. Based on these results, a method using a dome-neck aspect ratio (AR) parameter for the evaluation of the Spontaneous Thrombosis (ST) phenomena demonstrates the promising potentiality of this LBGM for clinical applications.
Copyright © 2013. Published by Elsevier B.V.

Entities:  

Keywords:  Anisotropic diffusion; Computed tomography angiography; Geodesic active contour; Giant intracranial aneurysm; Lattice Boltzmann method

Mesh:

Year:  2013        PMID: 24077409     DOI: 10.1016/j.media.2013.08.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  5 in total

1.  Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model.

Authors:  Yan Wang; Florent Seguro; Evan Kao; Yue Zhang; Farshid Faraji; Chengcheng Zhu; Henrik Haraldsson; Michael Hope; David Saloner; Jing Liu
Journal:  Med Image Anal       Date:  2017-05-19       Impact factor: 8.545

2.  Fully automatic segmentation of 4D MRI for cardiac functional measurements.

Authors:  Yan Wang; Yue Zhang; Wanling Xuan; Evan Kao; Peng Cao; Bing Tian; Karen Ordovas; David Saloner; Jing Liu
Journal:  Med Phys       Date:  2018-11-20       Impact factor: 4.071

3.  Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI.

Authors:  Yan Wang; Yue Zhang; Zhaoying Wen; Bing Tian; Evan Kao; Xinke Liu; Wanling Xuan; Karen Ordovas; David Saloner; Jing Liu
Journal:  Quant Imaging Med Surg       Date:  2021-04

4.  A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation.

Authors:  Fabien Lareyre; Cédric Adam; Marion Carrier; Carine Dommerc; Claude Mialhe; Juliette Raffort
Journal:  Sci Rep       Date:  2019-09-24       Impact factor: 4.379

5.  A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Authors:  Yingqian Liu; Zhuangzhi Yan
Journal:  Sensors (Basel)       Date:  2020-06-28       Impact factor: 3.576

  5 in total

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