Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Siriapisith, Thanongchai; Kusakunniran, Worapan; Haddawy, Peter

doi:10.1007/s10278-018-0049-z

Cited by 15 publications

(14 citation statements)

References 35 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior to the advent of machine-learning approaches, AAA segmentations were performed using intensity-based semi-automatic algorithms (level-sets, active shape models and graph cut methods) [10][11][12][13][14]. The primary drawback of these methods was the failure to accurately detect the outer boundary of the outer wall/aneurysm as the intensity of this region may be similar to that of adjacent structures.…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach to automate high-resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

Chandrashekar

Handa

Shivakumar

et al. 2020

European Heart Journal

View full text Add to dashboard Cite

Background Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vessel lumen. Pathological changes present within the blood lumen, vessel wall or a combination of both prevent accurate 3D reconstruction. In the example of aortic aneurysmal (AAA) disease, a blood clot or thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in 95% of cases. These deformations prevent the automatic extraction of vital clinically relevant information by current methods. Objectives In this study, we utilised deep learning segmentation methods to establish a high-throughput and automated segmentation pipeline for pathological blood vessels (ex. Aortic Aneurysm) in CT images acquired with or without the use of a contrast agent. Methods Twenty-six patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ethically-approved ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation (13/13). Data augmentation methods were implemented to diversify the training data set in a ratio of 10:1. We utilised a 3D U-Net with attention gating for both the aortic region-of-interest (ROI) detection and segmentation tasks. Trained architectures were evaluated using the DICE similarity score. Results Inter- and Intra- observer analysis supports the accuracy of the manual segmentations used for model training (intra-class correlation coefficient, “ICC” = 0.995 and 1.00, respective. P<0.001 for both). The performance of our Attention-based U-Net (DICE score: 94.8±0.5%) in extracting both the inner lumen and the outer wall of the aortic aneurysm from CT angiograms (CTA) was compared against a generic 3-D U-Net (DICE score: 89.5±0.6%) and displayed superior results (p<0.01). Fig 1A depicts the implementation of this network architecture within the aortic segmentation pipeline (automated ROI detection and aortic segmentation). This pipeline has allowed accurate and efficient extraction of the entire aortic volume from both contrast-enhanced CTA (DICE score: 95.3±0.6%) and non-contrast CT (DICE score: 93.2±0.7%) images. Fig 1B illustrates the model output alongside the labelled ground truth segmentation for the pathological aneurysmal region; only minor differences are visually discernible (coloured boxes). Conclusion We developed a novel automated pipeline for high resolution reconstruction of blood vessels using deep learning approaches. This pipeline enables automatic extraction of morphologic features of blood vessels and can be applied for research and potentially for clinical use. Automated Segmentation of Blood Vessels Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): University of Oxford Medical Research Fund, John Fell Fund

show abstract

Section: Discussionmentioning

confidence: 99%

A deep learning approach to automate high-resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

Chandrashekar

Handa

Shivakumar

et al. 2020

European Heart Journal

View full text Add to dashboard Cite

show abstract

“…However, these previous deep learning algorithms focused only on CT exams with contrast, while incidental identification of AAAs on scans without contrast is equally important but more challenging. Additionally, most of the previous works concentrated on the task of automated aortic segmentation [11,9,6], but there are very few studies investigating the more applied task of AAA detection, which has much greater clinical relevance than purely performing segmentation alone.…”

Section: Introductionmentioning

confidence: 99%

DeepAAA: Clinically Applicable and Generalizable Detection of Abdominal Aortic Aneurysm Using Deep Learning

Brooks

Hahn

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in the United States, is asymptomatic, often detected incidentally, and often missed by radiologists. Our model architecture is a modified 3D U-Net combined with ellipse fitting that performs aorta segmentation and AAA detection. The study uses 321 abdominal-pelvic CT examinations performed by Massachusetts General Hospital Department of Radiology for training and validation. The model is then further tested for generalizability on a separate set of 57 examinations with differing patient demographics and acquisition characteristics than the original dataset. DeepAAA achieves high performance on both sets of data (sensitivity/specificity 0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans and works with image volumes with varying numbers of images. We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection. It is expected that the model can serve as an effective background detector in routine CT examinations to prevent incidental AAAs from being missed.

show abstract

“…We created a feature-based expert system that combined the boundary propagation method with the active contour model to segment the vascular abdominal tree [ 17 ]. Siriapisith et al proposed an automatic detection of the outer wall of AAA using a graph cut based active contour method with a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques [ 27 ]. The method was tested in 20 CTA obtained from patients with AAA and the mean Dice score was 93.6%.…”

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

View full text Add to dashboard Cite

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.

show abstract

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Cited by 15 publications

References 35 publications

A deep learning approach to automate high-resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

A deep learning approach to automate high-resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

DeepAAA: Clinically Applicable and Generalizable Detection of Abdominal Aortic Aneurysm Using Deep Learning

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

Contact Info

Product

Resources

About