A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection

Cheng, Junlong; Tian, Shengwei; Yu, Long; Ma, Xiang; Xing, Yan

doi:10.1016/j.bspc.2020.102145

Cited by 31 publications

(18 citation statements)

References 9 publications

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“…The authors constructed a U-Net-based semantic segmentation architecture to segment the aortic lumen and performed aortic circularity analysis on the segmentation results. Their detection results exhibited 85.00% accuracy, 90.00% sensitivity, and 80.00% specificity 11 . In another study on aorta lumen segmentation, 260 type B aortic dissection patients were enrolled and a mean Dice coefficient exceeding 90% was recorded 12 .…”

Section: Introductionmentioning

confidence: 99%

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Lee

Hyuk

et al. 2022

Sci Rep

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Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.

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Section: Introductionmentioning

confidence: 99%

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Lee

Hyuk

et al. 2022

Sci Rep

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“…However, clinical validation showed that diameter measurements obtained through multi-planar reformations tend to fail or overestimate the aortic diameter in patients with aortic dissection or aneurysm, especially in presence of an aortic thrombus, where tracking methods fail (Kauffmann et al (2011); Krissian et al (2014)), or, track the two lumina separately (Krissian et al (2014)). Only recently, CNN-based segmentation approaches showed higher robustness also in presence of thrombus (Cao et al (2019); Hahn et al (2020); Cheng et al (2020); Chen et al (2021)).…”

Section: Ad Image Analysismentioning

confidence: 99%

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

Pepe¹,

Egger²,

Codari³

et al. 2021

Preprint

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Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the crosssectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations. Methodology: In this work, we combine convolutional neural networks and uncertainty quantification methods to predict the orientation of such cross-sectional planes. We use clinical data randomly processed by 11 operators for training, and test on a smaller set processed by 3 independent operators to assess interoperator variability. Results: Our analysis shows that manual selection of cross-sectional planes is characterized by 95% limits of agreement (LOA) of 10.6 • and 21.4 • per angle. Our method showed to decrease static error by 3.57 • (40.2%) and 4.11 • (32.8%) against state of the art and LOA by 5.4 • (49.0%) and 16.0 • (74.6%) against manual processing. Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.

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“…Early detection of MI is crucial for effective diagnosis and therapy to alleviate the MI risk that leads to death. Several techniques have been used for assessing MI include electrocardiogram (ECG) [3]- [5] computed tomography (CT) scan [6], [7] and magnetic resonance imaging (MRI) [8]- [11] In particular, cardiac MRI is the gold standard modality for assessing myocardial tissue providing comprehensive information on the myocardium's structures and functions [12]. Segmentation approaches are widely used in clinical CMR analysis to delineate the healthy and pathological contours of LV and myocardium.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

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A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection

Cited by 31 publications

References 9 publications

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

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