Automatic lumen segmentation in CT and PC-MR images of abdominal aortic aneurysm

Almuntashri, Ali; Finol, Ender A.; Agaian, Sos S.

doi:10.1109/icsmc.2012.6378232

Cited by 7 publications

(8 citation statements)

References 20 publications

(23 reference statements)

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“…The examined outcomes were variable. Four articles did not present any quantitative metrics of AI accuracy [ 32 , 33 , 34 , 35 ]. Two articles did not present suitable images for DSC calculation.…”

Section: Resultsmentioning

confidence: 99%

“…Only one study used a full noncontrast CT: a single AAA-positive case consisting of 145 slices. There were four studies that used mixed datasets (noncontrast and contrast-enhanced CT images) [ 23 , 30 , 32 , 33 ]. Two studies [ 23 , 30 ] used representative sets, consisting of 321 (with 232 slices per study on average) and 10 (each case consisted of 160 slices) studies with pathology rates of 77% and 20%, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

et al. 2022

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In this review, we focused on the applicability of artificial intelligence (AI) for opportunistic abdominal aortic aneurysm (AAA) detection in computed tomography (CT). We used the academic search system PubMed as the primary source for the literature search and Google Scholar as a supplementary source of evidence. We searched through 2 February 2022. All studies on automated AAA detection or segmentation in noncontrast abdominal CT were included. For bias assessment, we developed and used an adapted version of the QUADAS-2 checklist. We included eight studies with 355 cases, of which 273 (77%) contained AAA. The highest risk of bias and level of applicability concerns were observed for the “patient selection” domain, due to the 100% pathology rate in the majority (75%) of the studies. The mean sensitivity value was 95% (95% CI 100–87%), the mean specificity value was 96.6% (95% CI 100–75.7%), and the mean accuracy value was 95.2% (95% CI 100–54.5%). Half of the included studies performed diagnostic accuracy estimation, with only one study having data on all diagnostic accuracy metrics. Therefore, we conducted a narrative synthesis. Our findings indicate high study heterogeneity, requiring further research with balanced noncontrast CT datasets and adherence to reporting standards in order to validate the high sensitivity value obtained.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

et al. 2022

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“…Most of the previously published algorithms dedicated to the segmentation of the vascular system rely on expert systems and Lesage et al proposed an extensive review of 3D vessel lumen segmentation techniques, with various models, features and extraction schemes [ 23 ]. Among them, the region growing approaches and active contours methods are widely used to segment vascular regions [ 5 , 24 , 25 ]. Connecting morphological operators has advantages compared to traditional numerical solutions such as PDE approaches by being faster, having fewer parameters, and fewer numerical instability issues [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Lareyre

Adam

Carrier

et al. 2021

JCM

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Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

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“…Many different techniques may be applied in order to perform the segmentation of AAAs that can be classified according to the kind of information that guides the segmentation process. Thus, there are methods based on the raw intensity information of the image, such as clustering [ 4 ], multiscale [ 5 ], or histogram information [ 3 , 6 ]. There are methods based on the information provided by the intensity gradient that is used to control a deformable model.…”

Section: Introductionmentioning

confidence: 99%

Automated Delineation of Vessel Wall and Thrombus Boundaries of Abdominal Aortic Aneurysms Using Multispectral MR Images

Rodríguez-Vila

Tarjuelo-Gutiérrez

Sánchez-González

et al. 2015

Computational and Mathematical Methods in Medicine

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A correct patient-specific identification of the abdominal aortic aneurysm is useful for both diagnosis and treatment stages, as it locates the disease and represents its geometry. The actual thickness and shape of the arterial wall and the intraluminal thrombus are of great importance when predicting the rupture of the abdominal aortic aneurysms. The authors describe a novel method for delineating both the internal and external contours of the aortic wall, which allows distinguishing between vessel wall and intraluminal thrombus. The method is based on active shape model and texture statistical information. The method was validated with eight MR patient studies. There was high correspondence between automatic and manual measurements for the vessel wall area. Resulting segmented images presented a mean Dice coefficient with respect to manual segmentations of 0.88 and a mean modified Hausdorff distance of 1.14 mm for the internal face and 0.86 and 1.33 mm for the external face of the arterial wall. Preliminary results of the segmentation show high correspondence between automatic and manual measurements for the vessel wall and thrombus areas. However, since the dataset is small the conclusions cannot be generalized.

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Automatic lumen segmentation in CT and PC-MR images of abdominal aortic aneurysm

Cited by 7 publications

References 20 publications

Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Automated Delineation of Vessel Wall and Thrombus Boundaries of Abdominal Aortic Aneurysms Using Multispectral MR Images

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