123 related articles for article (PubMed ID: 34079839)
21. A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance.
Fadil H; Totman JJ; Hausenloy DJ; Ho HH; Joseph P; Low AF; Richards AM; Chan MY; Marchesseau S
J Cardiovasc Magn Reson; 2021 Apr; 23(1):47. PubMed ID: 33896419
[TBL] [Abstract][Full Text] [Related]
22. A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging.
Yang F; Zhang Y; Lei P; Wang L; Miao Y; Xie H; Zeng Z
Biomed Res Int; 2019; 2019():5636423. PubMed ID: 31467898
[TBL] [Abstract][Full Text] [Related]
23. Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.
Yang Y; Shah Z; Jacob AJ; Hair J; Chitiboi T; Passerini T; Yerly J; Di Sopra L; Piccini D; Hosseini Z; Sharma P; Sahu A; Stuber M; Oshinski JN
Front Radiol; 2023; 3():1144004. PubMed ID: 37492382
[TBL] [Abstract][Full Text] [Related]
24. Joint Segmentation and Uncertainty Estimation of Ventricular Structures from Cardiac MRI using a Bayesian CondenseUNet.
Hasan SMK; Linte CA
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():5047-5050. PubMed ID: 36085846
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.
Ghadimi S; Auger DA; Feng X; Sun C; Meyer CH; Bilchick KC; Cao JJ; Scott AD; Oshinski JN; Ennis DB; Epstein FH
J Cardiovasc Magn Reson; 2021 Mar; 23(1):20. PubMed ID: 33691739
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet).
Shao HC; Li T; Dohopolski MJ; Wang J; Cai J; Tan J; Wang K; Zhang Y
Phys Med Biol; 2022 Jun; 67(13):. PubMed ID: 35667374
[No Abstract] [Full Text] [Related]
29. L-CO-Net: Learned Condensation-Optimization Network for Segmentation and Clinical Parameter Estimation from Cardiac Cine MRI.
Kamrul Hasan SM; Linte CA
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1217-1220. PubMed ID: 33018206
[TBL] [Abstract][Full Text] [Related]
30. Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis.
Atehortúa A; Zuluaga MA; García JD; Romero E
Med Phys; 2016 Dec; 43(12):6270. PubMed ID: 27908177
[TBL] [Abstract][Full Text] [Related]
31. 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]
32. Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation.
Wibowo A; Triadyaksa P; Sugiharto A; Sarwoko EA; Nugroho FA; Arai H; Kawakubo M
J Imaging; 2022 Jul; 8(7):. PubMed ID: 35877637
[TBL] [Abstract][Full Text] [Related]
33. Calibration of cine MRI segmentation probability for uncertainty estimation using a multi-task cross-task learning architecture.
Hasan SMK; Linte CA
Proc SPIE Int Soc Opt Eng; 2022; 12034():. PubMed ID: 35634478
[TBL] [Abstract][Full Text] [Related]
34. Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning.
Xia Y; Ravikumar N; Greenwood JP; Neubauer S; Petersen SE; Frangi AF
Med Image Anal; 2021 Jul; 71():102037. PubMed ID: 33910110
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. A bidirectional registration neural network for cardiac motion tracking using cine MRI images.
Lu J; Jin R; Wang M; Song E; Ma G
Comput Biol Med; 2023 Jun; 160():107001. PubMed ID: 37187138
[TBL] [Abstract][Full Text] [Related]
37. Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension.
Sandfort V; Jacobs M; Arai AE; Hsu LY
Eur Radiol; 2021 Jun; 31(6):3941-3950. PubMed ID: 33247342
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks.
Kim T; Hedayat M; Vaitkus VV; Belohlavek M; Krishnamurthy V; Borazjani I
Quant Imaging Med Surg; 2021 May; 11(5):1763-1781. PubMed ID: 33936963
[TBL] [Abstract][Full Text] [Related]
40. Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.
Qiao M; Wang Y; Guo Y; Huang L; Xia L; Tao Q
Med Phys; 2020 Sep; 47(9):4189-4198. PubMed ID: 32564357
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]