Semi-Automatic Segmentation of Ct/Mri Images Based on Active Contour Method for 3D Reconstruction of Abdominal Aortic Aneurysms

Drapikowski, Paweł; Domagała, Zuzanna

doi:10.1515/ipc-2015-0002

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…An intensity-based approach followed by neural networks was used to detect the lumen and wall contours by Shum et al ( 15 ). Another contour-based segmentation approach to extract the aorta was proposed by Drapikowski and Domagala ( 16 ). An interactive segmentation model based on active shape was developed to extract the AAA tissues after manual segmentation of the first slice of the CT stack by de Bruijne et al ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abdolmanafi¹,

Forneris

Moore

et al. 2023

Front. Cardiovasc. Med.

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Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

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Section: Introductionmentioning

confidence: 99%

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abdolmanafi¹,

Forneris

Moore

et al. 2023

Front. Cardiovasc. Med.

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show abstract

“…Researchers have only a manual way to create solid models, which requires plenty of time and a sufficiently high qualification of the performer (as a rule, an engineer, rather than a medical professional) [5]. Partial automation of the process of constructing 3D models of biological objects is available only for creating surface images [6].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the process of developing human blood vessel models

2020

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Objective: to optimize the process of blood vessel biomechanical modeling via developing models of cerebral arterial circle. Materials and methods. Biomechanical modeling requires development of a patient-oriented three-dimensional (3D) solid-state geometric model of the object under study. This task can be resolved by computed tomography (CT) or magnetic resonance imaging (MRI). We developed the program that implements the construction of blood vessel contours from separate slices of MRI in a semi-automatic mode. These contours are exported as saved curves in a specific format to SolidWorks, where they are used to create 3D models of blood vessels. The models obtained this way take into account individual characteristics of the vascular system structure of a particular patient, and can be used in the process of biomechanical modeling. Results. The results of the program implementation of the recursive frontal growth method for processing 2D slices of tomograms are presented. Conclusion. The developed software allows semi-automatic loading of DICOM images and obtaining flat cross-sections (MRI slices) of vessels on their basis, as well as transferring them for further processing into computer-aided design systems.

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Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Czajkowska

Feinen

Grzegorzek

et al. 2015

Computerized Medical Imaging and Graphics

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Semi-Automatic Segmentation of Ct/Mri Images Based on Active Contour Method for 3D Reconstruction of Abdominal Aortic Aneurysms

Abstract: Abstract.Abstract The paper presents a CT/MRI image based semi-automatic AAA

Cited by 7 publications

References 11 publications

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Optimizing the process of developing human blood vessel models

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

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