Literature DB >> 11187511

3-D image analysis of abdominal aortic aneurysm.

M Subasic1, S Loncaric, E Sorantin.   

Abstract

In this paper we propose a technique for 3-D segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) angiography images. Output data form the proposed method can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D segmentation of CT images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the level-set algorithm instead of the classical active contour algorithm developed by Kass et al. The main advantage of the level set algorithm is that it enables easy segmentation surpassing most of the drawbacks of the classical approach. In the level-set approach for shape modeling, a 3-D surface is represented by a real 3-D function or equivalent 4-D surface. The 4-D surface is then evolved through an iterative process of solving the differential equation of surface motion. Surface motion is defined by velocity at each point. The velocity is a sum of constant velocity and curvature-dependent velocity. The stopping criterion is calculated based on image gradient. The algorithm has been implemented in MATLAB and C languages. Experiments have been performed using real patient CT angiography images and have shown good results.

Entities:  

Mesh:

Year:  2000        PMID: 11187511

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  5 in total

1.  Generic thrombus segmentation from pre- and post-operative CTA.

Authors:  Florent Lalys; Vincent Yan; Adrien Kaladji; Antoine Lucas; Simon Esneault
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-04-28       Impact factor: 2.924

2.  Development of a convolutional neural network to detect abdominal aortic aneurysms.

Authors:  Justin R Camara; Roger T Tomihama; Andrew Pop; Matthew P Shedd; Brandon S Dobrowski; Cole J Knox; Ahmed M Abou-Zamzam; Sharon C Kiang
Journal:  J Vasc Surg Cases Innov Tech       Date:  2022-05-02

3.  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

4.  Local aortic aneurysm wall expansion measured with automated image analysis.

Authors:  Jordan B Stoecker; Kevin C Eddinger; Alison M Pouch; Amey Vrudhula; Benjamin M Jackson
Journal:  JVS Vasc Sci       Date:  2021-12-08

Review 5.  An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.

Authors:  Jeffrey Liu; Bino Varghese; Farzaneh Taravat; Liesl S Eibschutz; Ali Gholamrezanezhad
Journal:  Diagnostics (Basel)       Date:  2022-05-30
  5 in total

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