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Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm

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Abstract

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.

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Acknowledgments

The authors gratefully acknowledge the Research Council of Kermanshah University of Medical Sciences (Grant Number: 96210) for the financial support. This work was performed in partial fulfillment of the requirements for Master. D. of Saba Mohammadi, in Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Conflict of interest

Saba Mohammadi has received research grant number 96210 from the Research Council of Kermanshah University of Medical Sciences. Vahab Dehlaghi has received research grant number 96210 from the Research Council of Kermanshah University of Medical Sciences. Mahdi Mohammadi and Arash Ahmadi declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  1. Students Research Committee, Kermanshah University of Medical Sciences (KUMS), Building No. 1, Shahid Beheshti Boulevard, Kermanshah, 6715847141, Iran

    Saba Mohammadi

  2. Department of Electrical Engineering, Faculty of Engineering, Sharif University of Technology, Azadi Ave, Tehran, 11365-11155, Iran

    Mahdi Mohammadi

  3. Department of Biomedical Engineering, Kermanshah University of Medical Sciences(KUMS), Building No. 1, Shahid Beheshti Boulevard, Kermanshah, 6715847141, Iran

    Vahab Dehlaghi

  4. Department of Electrical Engineering, Faculty of Engineering, Razi University, University Avenue, Taq-e Bostan, Kermanshah, 6714414971, Iran

    Arash Ahmadi

Authors
  1. Saba Mohammadi
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  2. Mahdi Mohammadi
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  3. Vahab Dehlaghi
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Corresponding author

Correspondence to Vahab Dehlaghi.

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Associate Editor Ajit P. Yoganathan oversaw the review of this article.

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Mohammadi, S., Mohammadi, M., Dehlaghi, V. et al. Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm. Cardiovasc Eng Tech 10, 490–499 (2019). https://doi.org/10.1007/s13239-019-00421-6

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  • DOI: https://doi.org/10.1007/s13239-019-00421-6

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