Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning

Cao, Long; Shi, Ruiqiong; Ge, Yangyang; Li, Xing; Zuo, Panli; Jia, Yan; Liu, Jie; He, Yuan; Wang, Xinhao; Luan, Shaoliang; Chai, Xiaodong; Guo, Wei

doi:10.1016/j.ejrad.2019.108713

Cited by 70 publications

(48 citation statements)

References 13 publications

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“…Furthermore, MS-Net, which was proposed to improve segmentation accuracy and precision, and significantly reduces the supervision cost, was proposed for full-resolution segmentation 14 . These previous works demonstrated satisfactory performance in specific vessel areas segmentation, such as aortic 15,16 , carotid 17,18 , or intracranial vessels 19 , which particularly inspired our study. However, complete head and neck CT scans that include three different vessels with different size magnitudes (aorta, carotid arteries, and intracranial blood vessels) make it difficult for models to capture cross-size-grade vessel characteristics.…”

Section: Discussionsupporting

confidence: 60%

Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

Fan

Ji²,

Zhang

et al. 2020

Nat Commun

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The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.

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Section: Discussionsupporting

confidence: 60%

Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

Fan

Ji²,

Zhang

et al. 2020

Nat Commun

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“…In Cao et al, multitask learning is exploited to segment the whole aorta, the true lumen and the false lumen using 3D CNNs. 1 The dataset is composed of 276 CTA scans.…”

Section: Introductionmentioning

confidence: 99%

3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks

Fantazzini

Espósito

Finotello

et al. 2020

Cardiovasc Eng Tech

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Purpose The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence. Methods A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence. Results The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation. Conclusion The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.

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“…The DCSs of the EA, TL, and the FL for 25 cases are shown in Figure 3 Figure 3B and C. The model worked well in the testing process, reaching mean DCS values of 0.958, 0.961, and 0.932 for EA, TL, and FL segmentation, respectively (Fig. 3C), which was significantly higher than that reported in a previous study (18). In order to further evaluate the performance of aorta segmentation in difference regions, we separated the aorta into the ascending aorta, aortic arch, descending aorta, and the abdominal aorta (Fig.…”

Section: Accuracy Of Segmentation By DLmentioning

confidence: 60%

“…Automated methods for aorta segmentation have been investigated extensively; most are based on classical imageprocessing technologies (20)(21)(22), and rarely on artificial neural networks (23,24). With the prevalence of DL in medical image segmentation (25,26), deep CNN has been proposed to segment the aorta based on 2D images in small datasets (18). Recently, Cao et al (18) applied a multi-task output CNN network for automatic TBAD segmentation, but kjronline.org only the accuracy of segmentation and volume measurement were assessed.…”

Section: Discussionmentioning

confidence: 99%

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A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection

Gao

Ji³

et al. 2021

Korean J Radiol

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Objective To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method ( r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were −0.042 mm (−3.412 to 3.330 mm), −0.376 mm (−3.328 to 2.577 mm), and 0.026 mm (−3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were −0.166 mm (−1.419 to 1.086 mm), −0.050 mm (−0.970 to 1.070 mm), and −0.085 mm (−1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

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Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning

Cited by 70 publications

References 13 publications

Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks

A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection

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