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Received：22 September 2021，
Revised：2021-12-10，
Accepted：17 December 2021，
Published：16 December 2022
移动端阅览
房颤是一种起源于心房的心脏疾病。据估计全球有超过3 000万人受其影响，虽然通过治疗可以降低患病风险，但房颤通常是隐匿的，很难及时诊断和干预。房颤的诊断方法主要有心脏触诊、光学体积描记术、血压监测振动法、心电图和基于影像的方法。房颤类型主要为阵发性房颤，前4种诊断方法不一定能捕捉到房颤发作，而且诊断周期长、成本高、准确率低及容易受医生的影响。左心房的解剖结构为房颤病理和研究进展提供了重要信息，基于医学影像的房颤分析需要准确分割左心房，通过分割结果计算房颤的临床指标，例如，射血分数、左心房体积、左心房应变及应变率，然后对左心房功能进行定量评估。采用影像的方法得出的诊断结果不易受人为干扰且具有处理大批量患者数据的能力，辅助医生及早发现房颤，对患者进行干预治疗，提高对房颤症状和临床诊断的认识，在临床实践中具有重大意义。本文将已有的分割方法归纳为传统方法、基于深度学习的方法以及传统与深度学习结合的方法。这些方法得到的结果为后续房颤分析提供了依据，但目前的分割方法许多都是半自动的，分割结果不够精确，训练数据集较小且依赖手工标注。本文总结了各种方法的优缺点，归纳了目前已有的公开数据集和房颤分析的临床应用，并展望了未来的发展趋势。
Atrial fibrillation (AF) is one of the most arrhythmia symptoms nowadays. The incidence rate of AF increases with elder growth and it can reach 10% population over 75 years old. The AF duration can be divided into paroxysmal
persistent and permanent
and it is induced to the morbidity and mortality of cardiovascular diseases severely. It affects more than 30 million people worldwide like reducing the quality of life and linking high risk of cerebral infarction and death. Although the risk can be reduced with appropriate treatment
AF is often latent and difficult to diagnose and intervene quickly. Recent AF-diagnostic methods have composed of cardiac palpation
optical plethysmography
blood pressure monitoring and vibration
electrocardiogram (ECG) and image-based methods. Most of atrial fibrillation has paroxysmal atrial fibrillation. The four diagnostic methods mentioned above may not capture the onset of atrial fibrillation. It is challenged for long-term diagnosis cycles
high costs
low accuracy and vulnerability. Medical imaging promotes contemporary modern medicine
computed tomography (CT) and magnetic resonance imaging (MRI) via transparent image of the cardiac anatomy. The MRI can be as one of the key medical imaging techniques
which of being unaffected by ionizing radiation
having high soft tissue contrast and high spatial resolution. Current images have limited of low signal-to-noise ratio (SNR) and low resolution to a certain extent. AF is regarded as a heart disease of atrial origin. In order to quantify the morphological and pathological changes of the left atrium (LA)
it is necessary to segment the LA derived from the medical image. The medical imaging analysis of AF requires accurate LA-related segmentation and quantitative evaluation of the function. The segmentation and functional evaluation of the LA is crucial to improving our understanding and diagnosis of AF. However
segmentation of the LA on medical images is still being challenged. 1) The LA can occupy a small proportion of the image only compared with the background of the image
making it difficult to locate and identify boundary details. 2) The strength of the LA is quite similar to its surrounding chambers
the myocardial wall is thinner
the quality of medical images is not high
the resolution is limited
and the boundaries often appear blurred or missing in the LA surrounding the pulmonary vein (PV). 3) The shapes and sizes of the LA vary significantly thematically as the number and topology of the PV. Our critical review is focused on the integration of current segmentation algorithms and traditional segmentation methods
deep learning based segmentation
and traditional & deep learning-integrated segmentation. Traditional segmentation methods are mainly composed of the active contour model (ACM)
atlas segmentation and threshold issue. ACM requires an accurate initial contour. Atlas segmentation requires complete multiple atlas sets and atlas registration
but the manual annotation of atlas sets is a challenging task due to a large number of atlas sets
which makes manual annotation difficult to be completed. In addition
the result of the annotation is vulnerable to be influenced by different taggers and atlas registration is very time-consuming. The threshold method requires the pre-determination of an appropriate threshold
which may be subjective and could ultimately limit the applicability and reproducibility. Although the traditional segmentation methods have achieved certain results
the accuracy of the segmentation is still insufficient. In recent years
deep learning technique has shown its potentials in medical image analysis
and they have qualified in different imaging modes and different clinical applications. It has improved imaging efficiency and quality
image analysis and interpretation and clinical evaluation. With the development of convolutional neural network (CNN)
many variant CNN models have emerged
which have made great impacts on the improvement of segmentation algorithms. The full convolutional network (FCN) is a variant of the CNN. Based on the CNN
the FCN uses the 1×1 convolutional layer to update the full connection layer
and changes the height and width of the feature maps of the intermediate layers back to the size of the input image in terms of transposing the convolutional layer
the prediction results and the input image have one-to-one correspondence in the spatial dimension
the FCN can accept input images of any size
and generate segmentation images of the same size. The FCN mainly uses three techniques: 1) convolution
2) upsampling and 3) skip connection. The FCN uses the skip connection structure to upsample feature maps of the last layer of the network model
and fused with feature maps of the shallow layer
combining the high-level semantic information with the low-level image information. The U-Net is a variant model of the FCN. The U-Net adopts the encoder-decoder architecture to form a U-shaped structure with four downsampling operations followed by four up sampling steps. The U-Net captures global features on the contraction path and achieves precise positioning on the extension path
thus the segmentation problem-solving of complex neuron structures has achieved excellent performance adequately. On this basis
variant models of the 3D U-Net and the Ⅴ-Net are introduced. The training of neural network models requires a large amount of labeled data as there are millions of parameters in the network that need to be optimized. Accurate segmentation of the LA is of great clinical significance for the diagnosis and analysis of AF. However
manual segmentation of the LA is time-consuming and prone to human-related errors. Therefore
the research of automatic segmentation algorithms is essential in assisting diagnosis and clinical decision-making. We summarize the pros and cons of varied segmentation strategies
existing public data sets and clinical applications of atrial fibrillation analysis and its future trends.
Bai W J, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, Glocker B, King A, Matthews P M and Rueckert D. 2017. Semi-supervised learning for network-based cardiac MR image segmentation//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, Canada: Springer: 253-260 [ DOI: 10.1007/978-3-319-66185-8_29 http://dx.doi.org/10.1007/978-3-319-66185-8_29 ]
Bai W J, Shi W Z, Ledig C and Rueckert D. 2015. Multi-atlas segmentation with augmented features for cardiac MR images. Medical Image Analysis, 19(1): 98-109 [DOI: 10.1016/j.media.2014.09.005]
Bai W J, Shi W Z, O'Regan D P, Tong T, Wang H Y, Jamil-Copley S, Peters N S and Rueckert D. 2013. A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Transactions on Medical Imaging, 32(7): 1302-1315 [DOI: 10.1109/TMI.2013.2256922]
Bian C, Yang X, Ma J Q, Zheng S, Liu Y A, Nezafat R, Heng P A and Zheng Y F. 2018. Pyramid network with online hard example mining for accurate left atrium segmentation//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granad, Spain: Springer: 237-245 [ DOI: 10.1007/978-3-030-12029-0_26 http://dx.doi.org/10.1007/978-3-030-12029-0_26 ]
Bratt A, Guenther Z, Hahn L D, Kadoch M, Adams P L, Leung A N C and Guo H H. 2019. Left atrial volume as a biomarker of atrial fibrillation at routine chest CT: deep learning approach. Radiology: Cardiothoracic Imaging, 1(5): #190057 [DOI: 10.1148/ryct.2019190057]
Chan T F and Vese L A. 2001. Active contours without edges. IEEE Transactions on Image Processing, 10(2): 266-277 [DOI: 10.1109/83.902291]
Chen C, Biffi C, Tarroni G, Petersen S, Bai W J and Rueckert D. 2019. Learning shape priors for robust cardiac MR Segmentation from multi-view images//Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention. Shenzhen, China: Springer: 523-531 [ DOI: 10.1007/978-3-030-32245-8_58 http://dx.doi.org/10.1007/978-3-030-32245-8_58 ]
Chugh S S, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin E J, Gillum R F, Kim Y H, McAnulty J H Jr, Zheng Z J, Forouzanfar M H, Naghavi M, Mensah G A, Ezzati M and Murray C J L. 2014. Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation, 129(8): 837-847 [DOI: 10.1161/CIRCULATIONAHA.113.005119]
ÇiçekÖ, Abdulkadir A, Lienkamp S S, Brox T and Ronneberger O. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens, Greece: Springer: 424-432 [ DOI: 10.1007/978-3-319-46723-8_49 http://dx.doi.org/10.1007/978-3-319-46723-8_49 ]
Cireşan D C, Giusti A, Gambardella L M and Schmidhuber J. 2012. Deep neural networks segment neuronal membranes in electron microscopy images//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: ACM: 2843-2851 [ doi: 10.5555/2999325.2999452 http://dx.doi.org/10.5555/2999325.2999452 ]
Daoudi A, Mahmoudi S and Chikh M A. 2013. Automatic segmentation of the left atrium on CT images//Proceedings of the 4th International Workshop on Statistical Atlases and Computational Models of the Heart. Nagoya, Japan: Springer: 14-23 [ DOI: 1007/978-3-642-54268-8_2 http://dx.doi.org/1007/978-3-642-54268-8_2 ]
Donal E, Galli E and Schnell F. 2017. Left atrial strain: a must or a plus for routine clinical practice? Circulation: Cardiovascular Imaging, 10(10): #007023 [DOI: 10.1161/CIRCIMAGING.117.007023]
Dou Q, Chen H, Yu L Q, Qin J and Heng P A. 2017. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7): 1558-1567 [DOI: 10.1109/TBME.2016.2613502]
Gan G C H, Ferkh A, Boyd A and Thomas L. 2018. Left atrial function: evaluation by strain analysis. Cardiovascular Diagnosis and Therapy, 8(1): 29-46 [DOI: 10.21037/cdt.2017.06.08]
Gao Y, Gholami B, Macleod R S, Blauer J, Haddad W M and Tannenbaum A R. 2010. Segmentation of the endocardial wall of the left atrium using local region-based active contours and statistical shape learning//Proceedings of SPIE 7632, Medical Imaging 2010: Image Processing. International Society for Optics and Photonics. San Diego, USA: SPIE: #76234Z [ DOI: 10.1117/12.844321 http://dx.doi.org/10.1117/12.844321 ]
Ghosh S,Ray N, Boulanger P, Punithakumar K and Noga M. 2020. Automated left atrial segmentation from magnetic resonance image sequences using deep convolutional neural network with autoencoder//Proceedings of the 17th IEEE International Symposium on Biomedical Imaging (ISBI). Iowa City, USA: IEEE: 1756-1760 [ DOI: 10.1109/ISBI45749.2020.9098646 http://dx.doi.org/10.1109/ISBI45749.2020.9098646 ]
Gonzales R A, Seemann F, Lamy J, Arvidsson P M, Heiberg E, Murray V and Peters D C. 2021. Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours. BMC Medical Imaging, 21(1): #101 [DOI: 10.1186/s12880-021-00630-3]
Grosgeorge D, Petitjean Cand Ruan S. 2014. Joint segmentation of right and left cardiac ventricles using multi-label graph cut//Proceedings of the 11th IEEE International Symposium on Biomedical Imaging. Beijing, China: IEEE: 429-432 [ DOI: 10.1109/ISBI.2014.6867900 http://dx.doi.org/10.1109/ISBI.2014.6867900 ]
Hammouda K, Khalifa F, Abdeltawab H, Elnakib A, Giridharan G A, Zhu M, Ng C K, Dassanayaka S, Kong M, Darwish H E, Mohamed T M A, Jones S P and El-Baz A. 2020. A new framework for performing cardiac strain analysis from cine MRI imaging in mice. Scientific Reports, 10(1): #7725 [DOI: 10.1038/s41598-020-64206-x]
Hao Y F, Liu J Q, Duan Y Y, Zhang X Q, Yu C S, Jiang T Z and Fan Y. 2012. Local label learning (L3) for multi-atlas based segmentation//Proceedings of SPIE 8314, Medical Imaging 2012: Image Processing. San Diego, USA: SPIE: #83142E [ DOI: 10.1117/12.911014 http://dx.doi.org/10.1117/12.911014 ]
Heckemann R A, Hajnal J V, Aljabar P, Rueckert D and Hammers A. 2006. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33(1): 115-126 [DOI: 10.1016/j.neuroimage.2006.05.061]
Hinojar R, Zamorano J L, Fernández-Méndez M, Esteban A, Plaza-Martin M, González-Gómez A, Carbonell A, Rincón L M, Nácher J J J and Fernández-Golfín C. 2019. Prognostic value of left atrial function by cardiovascular magnetic resonance feature tracking in hypertrophic cardiomyopathy. The International Journal of Cardiovascular Imaging, 35(6): 1055-1065 [DOI: 10.1007/s10554-019-01534-8]
January C T, Wann L S, Alpert J S, Calkins H, Cigarroa J E, Cleveland J C Jr, Conti J B, Ellinor P T, Ezekowitz M D, Field M E, Murray K T, Sacco R L, Stevenson W G, Tchou P J, Tracy C M and Yancy C W. 2014. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task force on practice guidelines and the heart rhythm society. Circulation, 130(23): 199-267 [DOI: 10.1161/CIR.0000000000000041]
Kadappu K K, Abhayaratna K, Boyd A, French J K, Xuan W, Abhayaratna W and Thomas L. 2016. Independent echocardiographic markers of cardiovascular involvement in chronic kidney disease: the value of left atrial function and volume. Journal of the American Society of Echocardiography, 29(4): 359-367 [DOI: 10.1016/j.echo.2015.11.019]
Kamnitsas K, Ledig C, Newcombe V F J, Simpson J P, Kane A D, Menon D K, Rueckert D and Glocker B. 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36: 61-78 [DOI: 10.1016/j.media.2016.10.004]
Kareem M, Lei N R, Ali A, Ciaccio E J, Acharya U R and Faust O. 2021. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomedical Signal Processing and Control, 69: #102818 [DOI: 10.1016/J.BSPC.2021.102818]
Karim R, Arujuna A, Housden R J, Gill J, Cliffe H, Matharu K, Gill J, Rindaldi C A, O'Neill M, Rueckert D, Razavi R, Schaeffter T and Rhode K. 2014. A method to standardize quantification of left atrial scar from delayed-enhancement MR images. IEEE Journal of Translational Engineering in Health and Medicine, 2: 1-15 [DOI: 10.1109/JTEHM.2014.2312191]
Karim R, Housden R J, Balasubramaniam M, Chen Z, Perry D, Uddin A, Al-Beyatti Y, Palkhi E, Acheampong P, Obom S, Hennemuth A, Lu Y L, Bai W J, Shi W Z, Gao Y, Peitgen H O, Radau P, Razavi R, Tannenbaum A, Rueckert D, Cates J, Schaeffter T, Peters D, MacLeod R and Rhode K. 2013. Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. Journal of Cardiovascular Magnetic Resonance, 15(1): #105 [DOI: 10.1186/1532-429X-15-105]
Kass M, Witkin A and Terzopoulos D. 1988. Snakes: active contour models. International Journal of Computer Vision, 1(4): 321-331 [DOI: 10.1007/BF00133570]
Khened M, Kollerathu V A and Krishnamurthi G. 2019. Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Medical Image Analysis, 51: 21-45 [DOI: 10.1016/j.media.2018.10.004]
Kim S J W, Seo S, Kim H S, Kim D Y, Kang K W, Min J J and Lee J S. 2019. Multi-atlas cardiac PET segmentation. Physica Medica, 58: 32-39 [DOI: 10.1016/j.ejmp.2019.01.003]
Li C Z, Tong Q Q, Liao X Y, Si W X, Sun Y Z, Wang Q and Heng P A. 2018. Attention based hierarchical aggregation network for 3D left atrial segmentation//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 255-264 [ DOI: 10.1007/978-3-030-12029-0_28 http://dx.doi.org/10.1007/978-3-030-12029-0_28 ]
Li L, Weng X, Schnabel J A and Zhuang X H. 2020a. Joint left atrial segmentation and scar quantification based on a DNN with spatial encoding and shape attention//Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Lima, Peru: Springer: 118-127 [ DOI: 10.1007/978-3-030-59719-1_12 http://dx.doi.org/10.1007/978-3-030-59719-1_12 ]
Li L, Wu F P, Yang G, Xu L C, Wong T, Mohiaddin R, Firmin D, Keegan J and Zhuang X H. 2020b. Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Medical Image Analysis, 60: #101595 [DOI: 10.1016/j.media.2019.101595]
Li L, Zimmer V A, Schnabel J A and Zhuang X H. 2021. AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs//Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France: Springer: 557-566 [ DOI: 10.1007/978-3-030-87231-1_54 http://dx.doi.org/10.1007/978-3-030-87231-1_54 ]
Li L, Zimmer V A, Schnabel J A and Zhuang X H. 2022. Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: a review. Medical Image Analysis, 77: #102360 [DOI: 10.1016/j.media.2022.102360]
Li Q, Hou J T and Yu B. 2021. The effect of pacemaker reactive anti-tachycardia pacing on atrial high-frequency events. Chinese Journal of Cardiac Pacing and Electrophysiology, 35(3): 214-219
李倩, 侯锦婷, 于波. 2021. 起搏器抗心动过速起搏干预心房高频事件的疗效分析. 中国心脏起搏与心电生理杂志, 35(3): 214-219 [DOI: 10.13333/j.cnki.cjcpe.2021.03.005]
Liu C, Lin N, Cao Y J and Yang C. 2021. Seg-CapNet: neural network model for the cardiac MRI segmentation. Journal of Image and Graphics, 26(2): 452-463
刘畅, 林楠, 曹仰杰, 杨聪. 2021. Seg-CapNet: 心脏MRI图像分割神经网络模型. 中国图象图形学报, 26(2): 452-463 [DOI: 10.11834/jig.190626]
Liu X, Shen Y, Zhao X and Zhang S. 2017. Quantized segmentation of fibrotic tissue of left atrial from delay-enhancement MRI images using level-set and graph-cut//Proceedings of 2017 International Conference on Machine Vision and Information Technology (CMVIT). Singapore, Singapore: IEEE: 23-27 [ DOI: 10.1109/CMVIT.2017.13 http://dx.doi.org/10.1109/CMVIT.2017.13 ]
Liu Y S, Wang K Q, Luo G G and Zhang H G. 2019. A framework for left atrium segmentation on CT images with combined detection network and level set model//Proceedings of 2019 Computing in Cardiology (CinC). Singapore, Singapore: IEEE: 1-4 [ DOI: 10.23919/CinC49843.2019.9005853 http://dx.doi.org/10.23919/CinC49843.2019.9005853 ]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE: 3431-3440 [ DOI: 10.1109/CVPR.2015.7298965 http://dx.doi.org/10.1109/CVPR.2015.7298965 ]
Loureno A, Kerfoot E, Dibblin C, Alskaf E, Anjari M, Bharath A A, King A P, Chubb H, Correia T M and Varela M. 2021. Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of cine MRI//Proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart. Lima, Peru: Springer: 137-145 [ DOI: 10.1007/978-3-030-68107-4_14 http://dx.doi.org/10.1007/978-3-030-68107-4_14 ]
Luo G N, Dong S Y, Wang K Q, Zuo W M, Cao S D and Zhang H G. 2018. Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images. IEEE Transactions on Biomedical Engineering, 65(9): 1924-1934 [DOI: 10.1109/TBME.2017.2762762]
Luo X D, Chen J N, Song T and Wang G T. 2021. Semi-supervised medical image segmentation through dual-task consistency//Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI: 8801-8809
Ma C, Luo G N and Wang K Q. 2017. A combined random forests and active contour model approach for fully automatic segmentation of the left atrium in volumetric MRI. Biomed Research International, 2017: #8381094 [DOI: 10.1155/2017/8381094]
Margeta J, McLeod K, Criminisi A and Ayache N. 2013. Decision forests for segmentation of the left atrium from 3D MRI//Proceedings of the 4th International Workshop on Statistical Atlases and Computational Models of the Heart. Nagoya, Japan: Springer: 49-56 [ DOI: 10.1007/978-3-642-54268-8_6 http://dx.doi.org/10.1007/978-3-642-54268-8_6 ]
Milletari F, Navab N and Ahmadi S A. 2016. Ⅴ-net: fully convolutional neural networks for volumetric medical image segmentation//Proceedings of the 4th International Conference on 3D Vision (3DV). Stanford, USA: IEEE: 565-571 [ DOI: 10.1109/3DV.2016.79 http://dx.doi.org/10.1109/3DV.2016.79 ]
Morales M A, Van Den Boomen M, Nguyen C, Kalpathy-Cramer J, Rosen B R, Stultz C M, Izquierdo-Garcia D and Catana C. 2021. DeepStrain: a deep learning workflow for the automated characterization of cardiac mechanics. Frontiers in Cardiovascular Medicine, 8(3): #730316 [DOI: 10.1101/2021.01.05.425266]
Mortazi A, Burt J and Bagci U. 2017. Multi-planar deep segmentation networks for cardiac substructures from MRI and CT//Proceedings of the 8th International Workshop on Statistical Atlases andComputational Models of the Heart. Quebec City, Canada: Springer: 199-206 [ DOI: 10.1007/978-3-319-75541-0_21 http://dx.doi.org/10.1007/978-3-319-75541-0_21 ]
Mulder H W, Van Stralen M, Ren B, Haak A, Van Burken G, Viergever M A, Bosch J G and Pluim J P W. 2018. Selection strategies for atlas-based mosaicing of left atrial 3-D transesophageal echocardiography data. Ultrasound in Medicine and Biology, 44(7): 1533-1543 [DOI: 10.1016/j.ultrasmedbio.2018.02.004]
Nakamori S, Ngo L H, Tugal D, Manning W J and Nezafat R. 2018. Incremental value of left atrial geometric remodeling in predicting late atrial fibrillation recurrence after pulmonary vein isolation: a cardiovascular magnetic resonance study. Journal of the American Heart Association, 7(19): #009793 [DOI: 10.1161/JAHA.118.009793]
Ngo T A, Lu Z and Carneiro G. 2017. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Medical Image Analysis, 35: 159-171 [DOI: 10.1016/j.media.2016.05.009]
Nie D, Gao Y Z, Wang L and Shen D G. 2018. ASDNet: attention based semi-supervised deep networks for medical image segmentation//Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention. Granada, Spain: Springer: 370-378 [ DOI: 10.1007/978-3-030-00937-3_43 http://dx.doi.org/10.1007/978-3-030-00937-3_43 ]
Nuñez-Garcia M, Zhuang X H, Sanroma G, Li L, Xu L C, Butakoff C and Camara O. 2018. Left atrial segmentation combining multi-atlas whole heart labeling and shape-based atlas selection//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 302-310 [ DOI: 10.1007/978-3-030-12029-0_33 http://dx.doi.org/10.1007/978-3-030-12029-0_33 ]
Pedersen K B, Madsen C, Sandgaard N C F, Hey T M, Diederichsen A C P, Bak S and Brandes A. 2019. Left atrial volume index and left ventricular global longitudinal strain predict new-onset atrial fibrillation in patients with transient ischemic attack. The International Journal of Cardiovascular Imaging, 35(7): 1277-1286 [DOI: 10.1007/s10554-019-01586-w]
Qiao M Y, Wang Y Y, Van Der Geest R J and Tao Q. 2018. Fully automated left atrium cavity segmentation from 3D GE-MRI by multi-atlas selection and registration//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 230-236 [ DOI: 10.1007/978-3-030-12029-0_25 http://dx.doi.org/10.1007/978-3-030-12029-0_25 ]
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Saini K, Dewal M L and Rohit M. 2012. A fast region-based active contour model for boundary detection of echocardiographic images. Journal of Digital Imaging, 25(2): 271-278 [DOI: 10.1007/s10278-011-9408-8]
Savioli N, Montana G and Lamata P. 2018. Ⅴ-FCNN: volumetric fully convolution neural network for automatic atrial segmentation//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 273-281 [ DOI: 10.1007/978-3-030-12029-0_30 http://dx.doi.org/10.1007/978-3-030-12029-0_30 ]
Shen W H, Xu W B, Zhang H Y, Sun Z X, Ma J X, Ma X L, Zhou S J, Guo S J and Wang Y Q. 2021. Automatic segmentation of the femur and tibia bones from x-ray images based on pure dilated residual U-Net. Inverse Problems and Imaging, 15(6): 1333-1346 [DOI: 10.3934/ipi.2020057]
Shi W Z, Zhuang X H, Wolz R, Simon D, Tung K P, Wang H Y, Ourselin S, Edwards P, Razavi R and Rueckert D. 2011. A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation//Proceedings of the 2nd International Workshop on Statistical Atlases and Computational Models of the Heart. Toronto, Canada: Springer: 178-187 [ DOI: 10.1007/978-3-642-28326-0_18 http://dx.doi.org/10.1007/978-3-642-28326-0_18 ]
Sodergren T, Bhalodia R, Whitaker R, Cates J, MarroucheN and Elhabian S. 2019. Mixture modeling of global shape priors and autoencoding local intensity priors for left atrium segmentation//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 357-367 [ DOI: 10.1007/978-3-030-12029-0_39 http://dx.doi.org/10.1007/978-3-030-12029-0_39 ]
Tao Q, Ipek E G, Shahzad R, Berendsen F F, Nazarian S and Van Der Geest R J. 2016. Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: towards objective atrial scar assessment. Journal of Magnetic Resonance Imaging, 44(2): 346-354 [DOI: 10.1002/jmri.25148]
Tobon-Gomez C, Geers A J, Peters J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y F, Zuluaga M A, Betancur J, Ayache N, Chikh M A, Dillenseger J L, Kelm B M, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R and Rhode K S. 2015. Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Transactions on Medical Imaging, 34(7): 1460-1473 [DOI: 10.1109/TMI.2015.2398818]
Tops L F, Schalij M J and Bax J J. 2010. Imaging and atrial fibrillation: the role of multimodality imaging in patient evaluation and management of atrial fibrillation. European Heart Journal, 31(5): 542-551 [DOI: 10.1093/eurheartj/ehq005]
Truong V T, Palmer C, Wolking S, Sheets B, Young M, Ngo T N M, Taylor M, Nagueh S F, Zareba K M, Raman S and Mazur W. 2020. Normal left atrial strain and strain rate using cardiac magnetic resonance feature tracking in healthy volunteers. European Heart Journal-Cardiovascular Imaging, 21(4): 446-453 [DOI: 10.1093/ehjci/jez157]
Veni G, Fu Z S, Awate S P and Whitaker R T. 2013. Bayesian segmentation of atrium wall using globally-optimal graph cuts on 3D meshes//Proceedings of the 23rd International Conference on Information Processing in Medical Imaging. Asilomar, USA: Springer: 656-667 [ DOI: 10.1007/978-3-642-38868-2_55 http://dx.doi.org/10.1007/978-3-642-38868-2_55 ]
Vesal S, Ravikumar N and Maier A. 2018. Dilated convolutions in neural networks for left atrial segmentation in 3D gadolinium enhanced-MRI//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 319-328 [ DOI: 10.1007/978-3-030-12029-0_35 http://dx.doi.org/10.1007/978-3-030-12029-0_35 ]
Wang C J, MacGillivray T, Macnaught G, Yang G and Newby D. 2018. A two-stage 3D Unet framework for multi-class segmentation on full resolution image[EB/OL]. [2021-04-12] . https://arxiv.org/pdf/1804.04341.pdf https://arxiv.org/pdf/1804.04341.pdf
Wang H Z, Suh J W, Das S R, Pluta J B, Craige C and Yushkevich P A. 2013. Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3): 611-623 [DOI: 10.1109/TPAMI.2012.143]
Wang Y Q and Jia Y D. 2007. A novel approach for segmentation of cardiac magnetic resonance images. Chinese Journal of Computers, 30(1): 129-136
王元全, 贾云得. 2007. 一种新的心脏核磁共振图像分割方法. 计算机学报, 30(1): 129-136 [DOI: 10.3321/j.issn:0254-4164.2007.01.015]
Wang Y Q and Jia Y D. 2009. Method for segmentation of the endocardium and epicardium of the left ventricle in cardiac magnetic resonance images. Journal of Software, 20(5): 1176-1184
王元全, 贾云得. 2009. 一种心脏核磁共振图像左室壁内、外膜分割方法. 软件学报, 20(5): 1176-1184 [DOI: 10.3724/SP.J.1001.2009.03557]
Wu X Y, Gou X K, Zhu Z Z, Wei Y L and Wang K. 2020. Left ventricular segmentation on ultrasound images using deep layer aggregation for residual dense networks. Journal of Image and Graphics, 25(9): 1930-1942
吴宣言, 缑新科, 朱子重, 魏域林, 王凯. 2020. 深层聚合残差密集网络的超声图像左心室分割. 中国图象图形学报, 25(9): 1930-1942 [DOI: 10.11834/jig.190552]
Wu Y W, Wang Y Q and Jia Y D. 2013. Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model. Computer Vision and Image Understanding, 117(9): 990-1003 [DOI: 10.1016/j.cviu.2012.12.008]
Xia Q, Yao Y X, Hu Z Q and Hao A M. 2018. Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks//Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart. Granada, Spain: Springer: 211-220 [ DOI: 10.1007/978-3-030-12029-0_23 http://dx.doi.org/10.1007/978-3-030-12029-0_23 ]
Xiong Z H, Fedorov V V, Fu X H, Cheng E, Macleod R and Zhao J C. 2019. Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Transactions on Medical Imaging, 38(2): 515-524 [DOI: 10.1109/TMI.2018.2866845]
Xiong Z H, Xia Q, Hu Z Q, Huang N, Bian C, Zheng Y F, Vesal S, Ravikumar N, Maier A, Yang X, Heng P A, Ni D, Li C Z, Tong Q Q, Si W X, Puybareau E, Khoudli Y, Géraud T, Chen C, Bai W J, Rueckert D, Xu L C, Zhuang X H, Luo X Z, Jia S M, Sermesant M, Liu Y S, Wang K Q, Borra D, Masci A, Corsi C, De Vente C, Veta M, Karim R, Preetha C J, Engelhardt S, Qiao M Y, Wang Y Y, Tao Q, Nuñez-Garcia M, Camara O, Savioli N, Lamata P and Zhao J C. 2021. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical Image Analysis, 67: #101832 [DOI: 10.1016/j.media.2020.101832]
Yang D, Zheng Y F and John M. 2013. Graph cuts based left atrium segmentation refinement and right middle pulmonary vein extraction in C-arm CT//Proceedings of SPIE 8669, Medical Imaging 2013: Image Processing. Lake Buena Vista, USA: SPIE: #86693U [ DOI: 10.1117/12.2007137 http://dx.doi.org/10.1117/12.2007137 ]
Yang G, Chen J, Gao Z F, Li S, Ni H, Angelini E, Wong T, Mohiaddin R, Nyktari E, Wage R, Xu L, Zhang Y P, Du X Q, Zhang H Y, Firmin D and Keegan J. 2020. Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Future Generation Computer Systems, 107: 215-228 [DOI: 10.1016/j.future.2020.02.005]
Yang H R, Sun J, Li H B, Wang L S and Xu Z B. 2018. Neural multi-atlas label fusion: application to cardiac MR images. Medical Image Analysis, 49: 60-75 [DOI: 10.1016/j.media.2018.07.009]
Yasuda R, Murata M, Roberts R, Tokuda H, Minakata Y, Suzuki K, Tsuruta H, Kimura T, Nishiyama N, Fukumoto K, Aizawa Y, Tanimoto K, Takatsuki S, Abe T and Fukuda K. 2015. Left atrial strain is a powerful predictor of atrial fibrillation recurrence after catheter ablation: study of a heterogeneous population with sinus rhythm or atrial fibrillation. European Heart Journal-Cardiovascular Imaging, 16(9): 1008-1014 [DOI: 10.1093/ehjci/jev028]
Yoon Y E, Oh I Y, Kim S A, Park K H, Kim S H, Park J H, Kim J E, Lee S P, Kim H K, Kim Y J, Sohn D W and Cho G Y. 2015. Echocardiographic predictors of progression to persistent or permanent atrial fibrillation in patients with paroxysmal atrial fibrillation (E6P Study). Journal of the American Society of Echocardiography, 28(6): 709-717 [DOI: 10.1016/j.echo.2015.01.017]
Yu L Q, Wang S J, Li X M, Fu C W and Heng P A. 2019. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation//Proceedings of 2019 International Conference on Medical Image Computing and Computer-Assisted Intervention. Shenzhen, China: Springer: 605-613 [ DOI: 10.1007/978-3-030-32245-8_67 http://dx.doi.org/10.1007/978-3-030-32245-8_67 ]
Zhang H Y, Zhang W X, Shen W H, Li N N, Chen Y J, Li S, Chen B, Guo S J and Wang Y Q. 2021. Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution. Biomedical Signal Processing and Control, 68: #102684 [DOI: 10.1016/j.bspc.2021.102684]
Zhang Y Z, Yang L, Chen J X, Fredericksen M, Hughes D P and Chen D Z. 2017. Deep adversarial networks for biomedical image segmentation utilizing unannotated images//Proceedings of 2017 International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, Canada: Springer: 408-416 [ DOI: 10.1007/978-3-319-66179-7_47 http://dx.doi.org/10.1007/978-3-319-66179-7_47 ]
Zhao Z, PuybareauÉ, Boutry N and Géraud T. 2021. Do not treat boundaries and regions differently: an example on heart left atrial segmentation//Proceedings of the 25th International Conference on Pattern Recognition (ICPR). Milan, Italy: IEEE: 7447-7453 [ DOI: 10.1109/ICPR48806.2021.9412755 http://dx.doi.org/10.1109/ICPR48806.2021.9412755 ]
Zheng Y F, Wang T Z, John M, Zhou S K, Boese J and Comaniciui D. 2011. Multi-part left atrium modeling and segmentation in C-arm CT volumes for atrial fibrillation ablation//Proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention. Toronto, Canada: Springer: 487-495 [ DOI: 10.1007/978-3-642-23626-6_60 http://dx.doi.org/10.1007/978-3-642-23626-6_60 ]
Zhu L J, Gao Y, Yezzi A and Tannenbaum A. 2013. Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior. IEEE Transactions on Image Processing, 22(12): 5111-5122 [DOI: 10.1109/TIP.2013.2282049]
Zhuang X H, Li L, Payer C, Štern D, Urschler M, Heinrich M P, Oster J, Wang C L, Smedby Ö, Bian C, Yang X, Heng P A, Mortazi A, Bagci U, Yang G Y, Sun C C, Galisot G, Ramel J Y, BrouardT, Tong Q Q, Si W X, Liao X Y, Zeng G D, Shi Z L, Zheng G Y, Wang C J, MacGillivray T, Newby D, Rhode K, Ourselin S, Mohiaddin R, Keegan J, Firmin D and Yang G. 2019. Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Medical Image Analysis, 58: #101537 [DOI: 10.1016/j.media.2019.101537]
Zhuang X H and Shen J. 2016. Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Medical Image Analysis, 31: 77-87 [DOI: 10.1016/j.media.2016.02.006]
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