DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography

Xu, Lu; Liu, Mingyuan; Shen, Zhenhua; Wang, Hua; Liu, Xiaowei; Wang, Xin; Wang, Siyu; Li, Tiefeng; Yu, Sangjie; Hou, Min; Guo, Jianhua; Zhang, Jicong; He, Yihua

doi:10.1016/j.compmedimag.2019.101690

Cited by 64 publications

(54 citation statements)

References 38 publications

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“…However, excessive network length may result in the appearance of grades in practical training. To address this problem, a skip connection between two U-nets (from the decoder layer of the first u-net to the encoder layer of the second one) was proposed [38], which has also been mentioned in [37]. In this study, we proposed a new between-net connection and compared the two connections in Section V.…”

Section: Related Work a Cnns For Medical Image Segmentationmentioning

confidence: 99%

“…As for the segmentation of fetal cardiac structures, Li Yu et al only segmented left ventricle in fetal echocardiographic sequences [34]. In [37], we proposed a DW-net, comprising a dilated convolutional chain (DCC) and a W-net, for A4C segmentation with a dataset of 895 A4C views. This method has the potential to accurately segment complex ultrasound multi-structured images when the data are not large.…”

Section: Cardiac Image Segmentationmentioning

confidence: 99%

“…In addition, we redesign an inter-network connection. In previous research [37], as mentioned in Section II, we proposed between-net connections (as BNC_DE) from the decoder layers of the first U-net to the encoder layers of the second one. In this study, for the better use of priors, we build between-net connections (as BNC_EE) from the encoder layers of the first U-net to the encoder layers of the second one.…”

Section: A Design Of the Cu-netmentioning

confidence: 99%

“…The U-net 2 is a cascaded network of two U-nets. The U-net + BNC_DE [37] connects intermediate layers in the decoder of the first U-net to the encoder of the second U-net based on the U-net 2 . The BNC_EE connects the intermediate layers in the encoder of the first U-net to the encoder of the second U-net based on the U-net 2 .…”

Section: Ablation Experimentsmentioning

confidence: 99%

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Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

Liu

Zhang

et al. 2020

IEEE Access

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An apical four-chamber (A4C) view from early fetal echocardiography is an extremely significant step in early diagnosis and timely treatment of congenital heart diseases. The objective is to perform automated segmentation of cardiac structures, namely, the epicardium, left ventricle, left atrium, descending aorta, right atrium, right ventricle, and thorax, in ultrasound A4C views in one shot in order to assist clinicians in prenatal examination. However, such a segmentation task is often faced with the following challenges: 1) low imaging resolution; 2) incomplete tissue boundary; 3) overall contrast of the image. To address these issues, in this study, we propose a cascaded U-net, named CU-net, with structural similarity index measure (SSIM) loss. First, the CU-net with two branch supervisions helps gain clear tissue boundaries and alleviate the gradient vanishing problem caused by increasing network depth. Second, between-net connections in the CU-net can transmit the prior information from the shallow layer to the deeper layer and obtain more refined segmentation results. Third, the method leverages on SSIM loss to preserve fine-grained structural information and obtain clear boundaries. Extensive experiments on a dataset of 1712 A4C views demonstrate that the proposed method achieves a high dice coefficient of 0.856, Hausdorff distance of 3.33, and pixel accuracy of 0.929, revealing its effectiveness and potential as a clinical tool. INDEX TERMS Semantic segmentation, convolutional neural network, apical four-chamber (A4C) view, fetal echocardiography.

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Section: Related Work a Cnns For Medical Image Segmentationmentioning

confidence: 99%

Section: Cardiac Image Segmentationmentioning

confidence: 99%

Section: A Design Of the Cu-netmentioning

confidence: 99%

Section: Ablation Experimentsmentioning

confidence: 99%

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Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

Liu

Zhang

et al. 2020

IEEE Access

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“…61 Xu et al recently developed a convolutional neural network model for the segmentation of cardiac structures from the fetal apical four-chamber view. 64 The authors hope to build off their initial work to create a future screening fetal echocardiography convolutional neural network model capable of autonomous prenatal detection of CHD lesions to reduce missed diagnoses and ultimately improve outcomes.…”

Section: Medical Imagingmentioning

confidence: 99%

A primer on artificial intelligence for the paediatric cardiologist

2020

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AbstractThe combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient’s profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient’s genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

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Diagnosis of fetal total anomalous pulmonary venous connection based on the post‐left atrium space ratio using artificial intelligence

et al. 2022

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Objective To explore whether the post‐left atrium space (PLAS) ratio would be useful for prenatal diagnosis of total anomalous pulmonary venous connection (TAPVC) using echocardiography and artificial intelligence. Methods We retrospectively included 642 frames of four‐chamber views from 319 fetuses (32 with TAPVC and 287 without TAPVC) in end‐systolic and end‐diastolic periods with multiple apex directions. The average gestational age was 25.6 ± 2.7 weeks. No other cardiac or extracardiac malformations were observed. The dataset was divided into a training set (n = 540; 48 with TAPVC and 492 without TAPVC) and test set (n = 102; 20 with TAPVC and 82 without TAPVC). The PLAS ratio was defined as the ratio of the epicardium‐descending aortic distance to the center of the heart‐descending aortic distance. Supervised learning was used in DeepLabv3+, FastFCN, PSPNet, and DenseASPP segmentation models. The area under the curve (AUC) was used on the test set. Results Expert annotations showed that this ratio was not related to the period or apex direction. It was higher in the TAPVC group than in the control group detected by the expert and the four models. The AUC of expert annotations, DeepLabv3+, FastFCN, PSPNet, and DenseASPP were 0.977, 0.941, 0.925, 0.856, and 0.887, respectively. Conclusion Segmentation models achieve good diagnostic accuracy for TAPVC based on the PLAS ratio.

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DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography

Cited by 64 publications

References 38 publications

Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

A primer on artificial intelligence for the paediatric cardiologist

Diagnosis of fetal total anomalous pulmonary venous connection based on the post‐left atrium space ratio using artificial intelligence

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