Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

Bonechi, Simone; Andreini, Paolo; Mecocci, A.; Giannelli, Nicola; Scarselli, Franco; Neri, Eugénio; Bianchini, Monica; Dimitri, Giovanna Maria

doi:10.3390/electronics10202559

Cited by 17 publications

(15 citation statements)

References 31 publications

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“…Several deep learning methods have been proposed for the vascular segmentation using Computed tomography (CT) [9][10][11] images and 4D flow MRI sequences [12][13][14] . Bonechi et al 9 compared U-Net and LinkNet 2D convolutional neural network (CNN) models for aorta segmentation by incorporating various pretrained encoders. A total of 154 CT scans with contrast mediums were considered.…”

Section: Description Of Purposementioning

confidence: 99%

Fully automated aortic segmentation of 3D phase-contrast magnetic resonance angiography images using deep learning techniques

Manokaran

Garcia

Ukwatta

2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Bicuspid aortic valve (BAV) is a hereditary disorder that develops in the fetus at the early stages of pregnancy. Though the patient may have BAV defect at the time of birth, it may not be diagnosed until the patient becomes often symptomatic in adulthood. BAV patients are at a higher risk of aneurysm growth with a high mortality rate. Hence, measurements acquired from automated aortic segmentation would aid in faster analysis of hemodynamic parameters for better risk-stratify in BAV patients. In this work, we propose a fully automated segmentation tool using a deep learning technique for fast and accurate aortic segmentation. The 3D aorta volume was segmented based on the proposed model (U-Net++) and compared with two-dimensional (2D) deep convolutional neural network (DCNN) models (U-Net and Attention U-Net). Performance metrics such as Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute volume difference (AVD) were used for model evaluation. The proposed model reported the highest DSC of 0.88±0.02 on the dataset comprising of 114 subjects (n=91 BAV and n=23 healthy cases). The HD shows a difference in mean of 3.8mm between the manual and the predicted results. Though a limited dataset was deployed in this work, the model reports a high DSC based on 3D phase contrast (PC) magnetic resonance angiogram (MRA) (PCMRA) images obtained at a clinical setting. This fully automated approach minimizes the burdensome data analysis, data annotation cost and would aid for early diagnosis and to start individualized treatment to enhance the patient outcome.

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Section: Description Of Purposementioning

confidence: 99%

Fully automated aortic segmentation of 3D phase-contrast magnetic resonance angiography images using deep learning techniques

Manokaran

Garcia

Ukwatta

2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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“…In their test results, they achieved a DSC of 0.92 ± 0.01. Bonechi et al (2021), in their study, proposed a fully automatic method for segmentation of the abdominal aorta [23]. They presented an automated method for segmentation of the aorta based on 2D CNN using 3D CT scans as input.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of the Aorta in CTA Images Using Deep Learning Methods

BOZKIR

BUDAK²,

KARATAŞ³

et al. 2023

Preprint

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Doctors manually segmenting patient CT images is both time-consuming and labor-intensive. Additionally, classic image processing techniques are insufficient in non-contrast CT images because the pixel values of tissues are very close. Automatic segmentation of the aorta in human anatomy can be a useful clinical application that can help reduce the workload of healthcare workers in preoperative planning. In this study, the segmentation of the thoracic aorta, abdominal aorta, and iliac arteries in contrast and non-contrast CT images was performed using U-Net, U-Net attention, and Inception U-Netv2 segmentation models. First, 2D axial images were extracted from all datasets. Preprocessing such as resizing, gray level normalization and histogram equalization were applied to the resulting axial images. The edge structures of the aortic structure were determined using the Contrast limited adaptive histogram equalization (Clahe) method. Then, 5-Fold Cross Validation was applied to the segmentation models to perform training and test operations. The resulting 2D sections from the test were merged to make a 3D structure and the spatial coordinate information of the original image was transferred to the predicted mask. The 3D image was improved by removing small objects incorrectly defined as negative around the 3D aortic segmentation obtained. In this study, the test results obtained from the Dongyang and KITS dataset, a U-Net model gave a 89.5% Dice, 81.0% IoU, 86.9% sensitivity, and 99% specificity score, a U-Net attention model gave a 89.7% Dice, 81.3% IoU, 87.3% sensitivity and 99% specificity score and Inception U-Netv2 model gave a 90.4% Dice, 82.7% IoU, 89.1% sensitivity and 99% specificity score. The Inception U-Netv2 model gave the highest predictive segmentation results.

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“…Several studies using deep learning methods have been proposed for aorta segmentation using CT images. [9][10][11][12] Not many methods have been proposed for aorta segmentation and quantification of blood flow parameters using 4D flow MRI images. Berhane et al 13 proposed a threedimensional (3D) convolutional neural network (CNN) model for aorta segmentation and analyzed flow parameters using phase-contrast magnetic resonance angiography (PCMRA) images.…”

Section: Introductionmentioning

confidence: 99%

Automated aortic segmentation and quantification of hemodynamic parameters from 4D flow MRI using deep learning techniques

Manokaran,

Garcia,

Ukwatta

2023

J. Med. Imag.

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To develop an automated method for aortic segmentation using deep learning techniques and further analyze the hemodynamic parameters in patients with bicuspid aortic valve (BAV). Since four-dimensional (4D) flow magnetic resonance imaging (MRI) imaging helps in analyzing and quantifying the blood flow changes that occur in aortic valve-related problems, such as BAV, 4D flow MRI images are considered.Approach: Our dataset consisted of 91 patients who had referral indications of BAV and 30 healthy volunteers who had no known cardiovascular disease. A U-Net++ with pretrained ResNet-34 encoders was trained for aortic segmentation using manual segmentation by an expert as the ground truth. In the first stage, the model was evaluated on 21 test cohorts using overlay and distance-based metrics, such as Dice score, Hausdorff distance, and absolute volume difference. In the second stage, the hemodynamic parameters, such as wall shear stress (WSS), viscous energy loss, and vorticity, were calculated to quantify the blood flow irregularities that occur in BAV patients. The segmentation and the flow parameters generated by the algorithm were compared with those generated using the manual segmentations. Paired t -test with alpha value of 0.05 was used for statistical significance testing.Results: As for overlap and distance-based metrics, the developed algorithm reported a Dice score coefficient of 0.90 AE 0.03, absolute volume difference of 1683 AE 1139 mm 3 , and Hausdorff distance of 3.2 AE 1.18 mm on test cohorts. The hemodynamic parameters calculated between automated and manual methods resulted in a mean difference of 6.62% for WSS with p-value of 0.94, 17.35% for mean viscous energy loss with p-value of 0.78, and 7.59% for vorticity with p-value of 0.97.Conclusions: A fast and accurate segmentation tool was developed for aortic segmentation using a dataset taken at clinical and blood flow parameters that were calculated based on the segmented aorta. These results will assist the clinicians to analyze the blood flow patterns and commence distinguished treatment in BAV patients.

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Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

Cited by 17 publications

References 31 publications

Fully automated aortic segmentation of 3D phase-contrast magnetic resonance angiography images using deep learning techniques

Fully automated aortic segmentation of 3D phase-contrast magnetic resonance angiography images using deep learning techniques

Segmentation of the Aorta in CTA Images Using Deep Learning Methods

Automated aortic segmentation and quantification of hemodynamic parameters from 4D flow MRI using deep learning techniques

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