Abdominal Aortic Aneurysm Segmentation from Contrast-Enhanced Computed Tomography Angiography Using Deep Convolutional Networks

Dziubich, Tomasz; Białas, Paweł; Znaniecki, Łukasz; Halman, Joanna; Brzeziński, Jakub

doi:10.1007/978-3-030-55814-7_13

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…21 22,23 Other groups have proposed models for the diagnosis of abdominal aortic aneurysm (AAA) with diagnostic accuracy of up to 99%. [25][26][27] These models collectively highlight the potential for machine learning applications in vascular surgery. A similarity of these prior studies is that each model used CNN-based architecture.…”

Section: Resultsmentioning

confidence: 99%

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Krebs,

Imran,

Fazzone

et al. 2024

Preprint

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Introduction: Machine learning techniques have shown excellent performance in 3D medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) utilizing SVS/STS-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between auTBAD patients based on rate of aortic growth. Methods: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two CT scans were analyzed: (1) the baseline CTA at index admission, and (2) either the most recent surveillance CTA, or the most recent CTA prior to an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase >5mm/year) and no/slow growth (diameter increase <5mm/year). Deidentified images were imported into an open-source software package for medical image analysis and randomly partitioned into training(80%), validation(10%), and testing(10%) cohorts. Training datasets were manually segmented based on SVS/STS criteria. A custom segmentation framework was used to generate the predicted segmentation output and aortic zone volumes. Results: Of 59 patients identified for inclusion, rapid growth was observed in 33 (56%) patients and no/slow growth was observed in 26 (44%) patients. There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (p>0.05 for all). Median duration between baseline and interval CT was 1.07 years (IQR 0.38-2.57). Post-discharge aortic intervention was performed in 13 (22%) of patients at a mean of 1.5+/- 1.2 years, with no difference between groups (p>0.05). In both groups, all zones of the thoracic and abdominal aorta increased in volume over time, with the largest relative increase in Zone 5 with a median 24% increase (IQR 4.4-37). Baseline zone 3 volumes were larger in the no/slow growth (6v3) than the rapid growth group (5v3) (p=0.03). There were no other differences in baseline zone volumes between groups (p>0.05 for all). Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in Zones 4 (0.82), 5(0.88), and 9(0.91). Conclusions: To our knowledge this is the first description of an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open-source, trained model demonstrates high concordance to the manually segmented aortas with the strongest performance in zones 4, 5, and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between rapid growth and no/slow growth patients.

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Section: Resultsmentioning

confidence: 99%

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Krebs,

Imran,

Fazzone

et al. 2024

Preprint

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“…The cross validation technique is often used for model testing in the case of limited sample size ( Krstajic et al, 2014 ). Dziubich et al (2020) proposed a CNN-based approach to AAA segmentation using 3D UNet, ResNet and VBNet. The training set was 8,126 images from 16 scans.…”

Section: Discussionmentioning

confidence: 99%

“…The test was evaluated in terms of the maximum diameter of the aorta with an average dice score of 91.0%. Dziubich et al (2020) proposed CNN-based AAA segmentation with an ensemble of 2D U-Net, ResNet and VBNet. The ensemble predictions from these frameworks were reported to have a dice score of 94.0%.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional neural networks (CNN) have recently been used for analyzing medical images in CT, MRI and ultrasound ( Cheng et al, 2016 ; Jackson et al, 2018 ; Litjens et al, 2017 ; Shen, Wu & Suk, 2017 ; Ronneberger, Fischer & Brox, 2015 ). However, there are yet only a few studies applying CNNs to AAA segmentation on computed tomographic angiography (CTA) ( Zheng et al, 2018 ; Lu et al, 2019 ; López-Linares et al, 2019 ; Salvi, Finol & Menon, 2021 ; Dziubich et al, 2020 ). The previous studies addressed only segmentation for post-operative endovascular aneurysm repair (EVAR) of AAA ( Freiman et al, 2010 ) with limited amounts of training data ( Krizhevsky, Sutskever & Hinton, 2012 ; Long & Shelhamer, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm

Siriapisith

Kusakunniran

Haddawy

2022

PeerJ Computer Science

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Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSV-UNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively.

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“…One of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In the paper "Abdominal Aortic Aneurysm segmentation from contrastenhanced computed tomography angiography using deep convolutional networks" [10], the authors address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter.…”

Section: Selected Papersmentioning

confidence: 99%

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Bellatreche

Bentayeb

Bieliková

et al. 2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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Abdominal Aortic Aneurysm Segmentation from Contrast-Enhanced Computed Tomography Angiography Using Deep Convolutional Networks

Cited by 9 publications

References 12 publications

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

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