Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (AAA) using convolutional neural networks and hough circles algorithm

Mohammadi, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1007/s13239-019-00421-6
  3. Publisher: Springer New York LLC , 2019
  4. Abstract:
  5. Purpose: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients’ lives due to its rupturing risk, so prompt recognition and diagnosis of this disorder is vital. Although computed tomography angiography (CTA) is the preferred imaging modality used by radiologist for diagnosing AAA, computed tomography (CT) images can be used too. In the recent decade, there has been several methods suggested by experts in order to find a precise automated way to diagnose AAA without human intervention base on CT and CTA images. Despite great approaches in some methods, most of them need human intervention and they are not fully automated. Also, the error rate needs to decrease in other methods. Therefore, finding a novel fully automated with lower error rate algorithm using CTA and CT images for Abdominal region segmentation, AAA detection, and disease severity classification is the main goal of this paper. Methods: The proposed method in this article will be performed in three steps: (1) designing a classifier based on Convolutional Neural Network (CNN) for classifying different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone. (2) After correct aorta detection, defining its edge and measuring its diameter with the use of Hough Circle Algorithm (which is an algorithm for finding an arbitrary shape in images and measuring its diameter in pixel) is the second step. (3) Ultimately, the detected aorta, depending on its diameter, will be categorized in one of these groups: (a) there is no risk of AAA, (b) there is a medium risk of AAA, and (c) there is a high risk of AAA. Results: The designed CNN classifier classifies different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone with the accuracy, precision, and sensitivity of 97.93, 97.94, and 97.93% respectively. The accuracy of the proposed classifier for aorta region detection is 98.62% and Hough Circles algorithm can classify 120 aorta patches according to their diameter with accuracy of 98.33%. Conclusions: As a whole, a classifier using Convolutional Neural Network is designed and applied in order to detect AAA region among other abdominal regions. Then Hough Circles algorithm is applied to aorta patches for finding aorta border and measuring its diameter. Ultimately, the detected aortas will be categorized according to their diameters. All steps meet the expected results. © 2019, Biomedical Engineering Society
  6. Keywords:
  7. Abdominal aortic aneurysm (AAA) ; Convolutional neural networks (CNN) ; CT images ; CTA images ; Hough circles algorithm ; The state of the art result ; Automation ; Blood vessels ; Bone ; Convolution ; Diagnosis ; Edge detection ; Image segmentation ; Neural networks ; Abdominal aortic aneurysms ; Convolutional neural network ; CT Image ; State of the art ; Computerized tomography ; Abdominal aorta ; Abdominal aortic aneurysm ; Accuracy ; Article ; Computed tomographic angiography ; Computer assisted tomography ; Convolutional neural network ; Deep learning ; Diagnostic accuracy ; Disease severity ; Disease severity assessment ; Hough circle algorithm ; Human ; Learning ; Learning algorithm ; Learning curve ; Mathematical model ; Nonparametric test ; Priority journal ; Random forest ; Reaction time ; Sensitivity and specificity
  8. Source: Cardiovascular Engineering and Technology ; Volume 10, Issue 3 , 2019 , Pages 490-499 ; 1869408X (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s13239-019-00421-6