3D image analysis of abdominal aortic aneurysm

Subašić, Marko; Lončarić, Sven; Sorantin, Erich

doi:10.1117/12.467138

Cited by 42 publications

(36 citation statements)

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“…Thrombus segmentation is a more difficult problem, complicated by regions of low boundary contrast and by many neighboring structures in close proximity to the aneurysm wall. Previously reported approaches yield inaccurate results [10] or need extensive user interaction [7].…”

Section: Introductionmentioning

confidence: 99%

Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images

Bruijne

Ginneken

Viergever

et al. 2003

Lecture Notes in Computer Science

112

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Abstract. Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.

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Section: Introductionmentioning

confidence: 99%

Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images

Bruijne

Ginneken

Viergever

et al. 2003

Lecture Notes in Computer Science

112

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“…Aneurysm volume measurement requires segmentation of the aneurysm outer boundary, which is a more complex task. A few authors have addressed this problem in CTA images, using three-dimensional active surfaces or level-sets [2,3,4]. To our knowledge, no research has concentrated on automated aneurysm volume measurements in MRI scans.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images

Bruijne

Ginneken

Bartels

et al. 2003

Lecture Notes in Computer Science

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Abstract. An automated method for segmenting the outer boundary of abdominal aortic aneurysms in MR images is presented. The method is based on the well known Active Shape Models (ASM), which fit a global landmark-based shape model on the basis of local boundary appearance models. The original threedimensional ASM scheme is modified to deal with multi-spectral image information and inconsistent boundary appearance in a principled way, with only a limited amount of training data. In addition, a framework for user interaction is proposed. If required, the obtained segmentation can be corrected in an interactive manner by indicating points on the desired boundary. The methods are evaluated in leave-one-out experiments on 21 datasets. A segmentation scheme combining gray level information from two or three MR sequences produces significantly better results than a single-scan model. Average volume errors with respect to the manual segmentation are 4.0%, in 19 out of 21 datasets. In the cases in which the obtained error is large, results can easily be improved using the interactive scheme.

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“…Bruijne et al [16] introduced an Adapting Active Shape Models (ASM) for tubular structure segmentation. Subasic et al [17] utilized the level-set algorithm for segmentation of abdominal aortic aneurysm (AAA). Though aortic segmentation has been repeatedly attempted in the past, it is believed this is the first study investigating its use for connective tissue disorders detection.…”

Section: Aortic Dissectionmentioning

confidence: 99%

Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis

Hansen

Zhao

Zhang

et al. 2006

Computer Vision Approaches to Medical Image Analysis

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The current report concerns methods of early detection of connective tissue disorders leading to aortic aneurysms and dissections. Automated and accurate segmentation of the aorta in 4D (3D + time) MR image data is reviewed, and a computer-aided diagnosis (CAD) method using independent component analysis is reported. This admits the objective identification of subjects with connective tissue disorders from 4D aortic MR images.The majority of the presented work is concentrated on independent component analysis(ICA), estimating sources to be used for the diagnosis task. Prior knowledge of the source distribution is utilized using an ordering of the components. Two new ordering measures are introduced in current work. A novel approach to constrained dimensionality reduction in ICA is developed. A new idea of time-invariant independent components is introduced, and assists in the disease detection in the presence of sparse data.4D MR image data sets acquired from 21 normal and 10 diseased subjects are used to evaluate the efficiency of the method. The automated 4D segmentation result produces accurate aortic surfaces. The ICA results are validated by a leave-one-out classification test, and are further substantiated by visual inspection of the components. Using a single phase of the cardiac cycle, 8 out of 10 diseased subjects are identified and the specificity is 100 %, classifying all 21 healthy subjects correctly. These results are obtained using components showing correspondence to clinical observations. With 4D information included, the CAD method classifies 9 out of 10 diseased correctly, and still the specificity is 100 %. ii ResuméDen indevaerende rapport vedrører metoder til tidlig detektering af bindevaevssygdomme, som fører til aortic aneurysms og dissections. En automatisk og praecis metode til segmentering af aorta i 4D (3D + tid) MR data er refereret og en computerassisteret diagnose (CAD) metode, der involverer brugen af independent component analysis, er rapporteret. Dette muliggør en objektiv identificering af subjekter med bindevaevssygdomme, udfra 4D MR billeder af aorta.Hovedparten af det fremlagte arbejde er koncentreret omkring independent component analysis (ICA), som estimerer kilder, der bruges under diagnose opgaven. A priori viden om kildernes fordeling er udnyttet til udformningen af en sortering af de fundne komponenter. To nye sortereringsmål er fremført i det indevaerende arbejde. En ny tilgang til dimsionsreducering under bibetingelser i ICA er udviklet. Et nyt koncept om en tidsinvariant independent component er desuden introduceret, hvilket assisterer til sygdomsdetekteringen, når der kun er en staerkt begraenset maengde data til rådighed.4D MR billedsaet, optaget af 21 normale og 10 syge subjekter, er brugt til at evaluere effektiviteten af den udviklede metode. Den automatiserede 4D segmentering giver en nøjagtig aorta overflade. ICA resultaterne er valideret ved en leave-one-out klassificeringstest, og er yderligere underbygget ved visuel inspektion af de fundne komponent...

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3D image analysis of abdominal aortic aneurysm

Cited by 42 publications

References 0 publications

Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images

Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images

Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images

Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis

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