Identification and Quantification of Cardiovascular Structures From CCTA

Baskaran, Lohendran; Maliakal, Gabriel; Al’Aref, Subhi J.; Singh, Gurpreet; Xu, Zhuoran; Michalak, Kelly; Dolan, Kristina; Gianni, Umberto; Rosendael, Alexander van; Hoogen, Inge van den; Han, Donghee; Stuijfzand, Wijnand J.; Pandey, Mohit; Lee, Benjamin C.; Lin, Fay Y.; Knaapen, Paul; Marques, Hugo; Bax, Jeroen J.; Berman, Daniel S.; Chang, Hyuk Jae; Shaw, Leslee J.; Min, James K.

doi:10.1016/j.jcmg.2019.08.025

Cited by 51 publications

(62 citation statements)

References 19 publications

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“…Whilst predicting 8 structures, this model omitted the four cardiac chambers and the left ventricular wall. However, these structures have previously been segmented by our group, and this current study is an extension of that work [13]. Although segmenting most of the great vessels, the current study does not segment the pulmonary veins.…”

Section: Plos Onementioning

confidence: 94%

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Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Baskaran

Al’Aref

Maliakal³

et al. 2020

PLoS ONE

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Section: Plos Onementioning

confidence: 94%

“…Eight similar but separate networks were trained for each structure. Prior work on other cardiovascular structures using this framework has previously been reported [13].…”

Section: Deep Learning Modelmentioning

confidence: 99%

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Baskaran

Al’Aref

Maliakal³

et al. 2020

PLoS ONE

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“…Among these techniques, atlas-based approaches have been widely used to segment numerous cardiac structures [5]- [19]. More recent works also incorporate deep learning-based approaches for automatic cardiac segmentation [20]- [24].…”

Section: Previous Workmentioning

confidence: 99%

“…Several other works have attempted to segment more cardiovascular structures. Zheng et al [28] and Baskaran et al [24] segmented the four chambers and the LVM. In Zuluaga et al [7] and Lu et al's [14] works, the four chambers, LVM, and AA were segmented.…”

Section: Previous Workmentioning

confidence: 99%

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

et al. 2020

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Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application. INDEX TERMS Computed tomography, heart segmentation, multi-atlas segmentation, random walk.

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“…More recently, fully convolutional networks (FCN) such as the ‘U-Net’ 20 have been shown to provide high pixel-wise segmentation accuracy and capture more global context. For example, a 2D U-Net with cross-entropy loss has shown the highest LV segmentation accuracy (Dice = 0.968) in the ‘ACDC’ dataset which is the largest publicly available cardiac MRI dataset; 21 Baskaran et al 22 successfully applied a 2D U-Net to segment four-chamber images in cardiac CT in a 2D slice-by-slice fashion with high accuracy (Dice > 0.91); and Vigneault et al 23 modified a conventional U-Net to also predict scaling and rotation of 2D MRI images. However, predicting SAX and LAX imaging planes requires significant global context.…”

Section: Introductionmentioning

confidence: 99%

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning

Chen

Rigolli

Vigneault

et al. 2021

European Heart Journal - Digital Health

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Aims To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac CT via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. Methods and Results 100 patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis (SAX) planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. DL bloodpool segmentations showed close agreement with manual LV (median Dice: 0.91, Hausdorff distance: 6.18mm) and LA (Dice: 0.93, HD: 7.35mm) segmentations and strong correlation with manual EF (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96mm) and angular orientation (7.96 °) errors which were comparable to inter-reader differences (p > 0.71). 84 – 97% of DL-prescribed LAX planes correctly intersected American Heart Association (AHA) segments, which was comparable (p > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. Conclusion A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

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Identification and Quantification of Cardiovascular Structures From CCTA

Cited by 51 publications

References 19 publications

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning

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