279 related articles for article (PubMed ID: 28437634)
1. Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.
Tan LK; Liew YM; Lim E; McLaughlin RA
Med Image Anal; 2017 Jul; 39():78-86. PubMed ID: 28437634
[TBL] [Abstract][Full Text] [Related]
2. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.
Khened M; Kollerathu VA; Krishnamurthi G
Med Image Anal; 2019 Jan; 51():21-45. PubMed ID: 30390512
[TBL] [Abstract][Full Text] [Related]
3. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression.
Tan LK; McLaughlin RA; Lim E; Abdul Aziz YF; Liew YM
J Magn Reson Imaging; 2018 Jul; 48(1):140-152. PubMed ID: 29316024
[TBL] [Abstract][Full Text] [Related]
4. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.
Avendi MR; Kheradvar A; Jafarkhani H
Med Image Anal; 2016 May; 30():108-119. PubMed ID: 26917105
[TBL] [Abstract][Full Text] [Related]
5. An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images.
Ma Y; Wang L; Ma Y; Dong M; Du S; Sun X
Int J Comput Assist Radiol Surg; 2016 Nov; 11(11):1951-1964. PubMed ID: 27295053
[TBL] [Abstract][Full Text] [Related]
6. A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.
Abdeltawab H; Khalifa F; Taher F; Alghamdi NS; Ghazal M; Beache G; Mohamed T; Keynton R; El-Baz A
Comput Med Imaging Graph; 2020 Apr; 81():101717. PubMed ID: 32222684
[TBL] [Abstract][Full Text] [Related]
7. Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.
Penso M; Moccia S; Scafuri S; Muscogiuri G; Pontone G; Pepi M; Caiani EG
Comput Methods Programs Biomed; 2021 Jun; 204():106059. PubMed ID: 33812305
[TBL] [Abstract][Full Text] [Related]
8. Automatic cardiac cine MRI segmentation and heart disease classification.
Ammar A; Bouattane O; Youssfi M
Comput Med Imaging Graph; 2021 Mar; 88():101864. PubMed ID: 33485057
[TBL] [Abstract][Full Text] [Related]
9. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Qin W; Wu Y; Li S; Chen Y; Yang Y; Liu X; Zheng H; Liang D; Hu Z
Biomed Phys Eng Express; 2020 Feb; 6(2):025009. PubMed ID: 33438635
[TBL] [Abstract][Full Text] [Related]
10. Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks.
Liao F; Chen X; Hu X; Song S
IEEE Trans Cybern; 2019 Feb; 49(2):495-504. PubMed ID: 29990055
[TBL] [Abstract][Full Text] [Related]
11. Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming.
Liu H; Hu H; Xu X; Song E
Acad Radiol; 2012 Jun; 19(6):723-31. PubMed ID: 22465463
[TBL] [Abstract][Full Text] [Related]
12. Fully Automatic initialization and segmentation of left and right ventricles for large-scale cardiac MRI using a deeply supervised network and 3D-ASM.
Hu H; Pan N; Frangi AF
Comput Methods Programs Biomed; 2023 Oct; 240():107679. PubMed ID: 37364366
[TBL] [Abstract][Full Text] [Related]
13. Automatic localization of the left ventricular blood pool centroid in short axis cardiac cine MR images.
Tan LK; Liew YM; Lim E; Abdul Aziz YF; Chee KH; McLaughlin RA
Med Biol Eng Comput; 2018 Jun; 56(6):1053-1062. PubMed ID: 29147835
[TBL] [Abstract][Full Text] [Related]
14. Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming.
Hu H; Liu H; Gao Z; Huang L
Magn Reson Imaging; 2013 May; 31(4):575-84. PubMed ID: 23245907
[TBL] [Abstract][Full Text] [Related]
15. Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study.
Tao Q; Yan W; Wang Y; Paiman EHM; Shamonin DP; Garg P; Plein S; Huang L; Xia L; Sramko M; Tintera J; de Roos A; Lamb HJ; van der Geest RJ
Radiology; 2019 Jan; 290(1):81-88. PubMed ID: 30299231
[TBL] [Abstract][Full Text] [Related]
16. Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI.
Wang Z; Xie L; Qi J
Magn Reson Imaging; 2020 Feb; 66():131-140. PubMed ID: 31465788
[TBL] [Abstract][Full Text] [Related]
17. Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach.
Wu B; Fang Y; Lai X
Comput Med Imaging Graph; 2020 Jun; 82():101719. PubMed ID: 32325284
[TBL] [Abstract][Full Text] [Related]
18. Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension.
Yan W; Wang Y; van der Geest RJ; Tao Q
Comput Biol Med; 2019 Aug; 111():103356. PubMed ID: 31323604
[TBL] [Abstract][Full Text] [Related]
19. Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging.
Leng S; Yang X; Zhao X; Zeng Z; Su Y; Koh AS; Sim D; Le Tan J; Tan RS; Zhong L
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():4500-4503. PubMed ID: 30441351
[TBL] [Abstract][Full Text] [Related]
20. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.
Ngo TA; Lu Z; Carneiro G
Med Image Anal; 2017 Jan; 35():159-171. PubMed ID: 27423113
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]