137 related articles for article (PubMed ID: 38640619)
1. CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.
Aghapanah H; Rasti R; Kermani S; Tabesh F; Banaem HY; Aliakbar HP; Sanei H; Segars WP
Comput Med Imaging Graph; 2024 Jul; 115():102382. PubMed ID: 38640619
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Fully automated cardiac MRI segmentation using dilated residual network.
Ahmad F; Hou W; Xiong J; Xia Z
Med Phys; 2023 Apr; 50(4):2162-2175. PubMed ID: 36395472
[TBL] [Abstract][Full Text] [Related]
4. An iterative multi-path fully convolutional neural network for automatic cardiac segmentation in cine MR images.
Ma Z; Wu X; Wang X; Song Q; Yin Y; Cao K; Wang Y; Zhou J
Med Phys; 2019 Dec; 46(12):5652-5665. PubMed ID: 31605627
[TBL] [Abstract][Full Text] [Related]
5. Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network.
He Y; Qin W; Wu Y; Zhang M; Yang Y; Liu X; Zheng H; Liang D; Hu Z
J Xray Sci Technol; 2020; 28(3):541-553. PubMed ID: 32176675
[TBL] [Abstract][Full Text] [Related]
6. A distance map regularized CNN for cardiac cine MR image segmentation.
Dangi S; Linte CA; Yaniv Z
Med Phys; 2019 Dec; 46(12):5637-5651. PubMed ID: 31598971
[TBL] [Abstract][Full Text] [Related]
7. A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.
Kunkyab T; Bahrami Z; Zhang H; Liu Z; Hyde D
J Appl Clin Med Phys; 2024 Mar; 25(3):e14297. PubMed ID: 38373289
[TBL] [Abstract][Full Text] [Related]
8. The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.
Wang J; Wang S; Liang W; Zhang N; Zhang Y
J Appl Clin Med Phys; 2022 May; 23(5):e13597. PubMed ID: 35363415
[TBL] [Abstract][Full Text] [Related]
9. CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation.
Shoieb DA; Fathalla KM; Youssef SM; Younes A
Phys Eng Sci Med; 2024 Mar; 47(1):153-168. PubMed ID: 37999903
[TBL] [Abstract][Full Text] [Related]
10. Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.
Xiao Z; Su Y; Deng Z; Zhang W
Comput Methods Programs Biomed; 2022 Nov; 226():107099. PubMed ID: 36116398
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Cross-convolutional transformer for automated multi-organs segmentation in a variety of medical images.
Wang J; Zhao H; Liang W; Wang S; Zhang Y
Phys Med Biol; 2023 Jan; 68(3):. PubMed ID: 36623323
[No Abstract] [Full Text] [Related]
13. Male pelvic multi-organ segmentation using token-based transformer Vnet.
Pan S; Lei Y; Wang T; Wynne J; Chang CW; Roper J; Jani AB; Patel P; Bradley JD; Liu T; Yang X
Phys Med Biol; 2022 Oct; 67(20):. PubMed ID: 36170872
[No Abstract] [Full Text] [Related]
14. Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI.
Chen Z; Lalande A; Salomon M; Decourselle T; Pommier T; Qayyum A; Shi J; Perrot G; Couturier R
Comput Med Imaging Graph; 2022 Jan; 95():102014. PubMed ID: 34864579
[TBL] [Abstract][Full Text] [Related]
15. DE-MRI myocardial fibrosis segmentation and classification model based on multi-scale self-supervision and transformer.
Ding Y; Xie W; Wong KKL; Liao Z
Comput Methods Programs Biomed; 2022 Nov; 226():107049. PubMed ID: 36274507
[TBL] [Abstract][Full Text] [Related]
16. SAUN: Stack attention U-Net for left ventricle segmentation from cardiac cine magnetic resonance imaging.
Sun X; Garg P; Plein S; van der Geest RJ
Med Phys; 2021 Apr; 48(4):1750-1763. PubMed ID: 33544895
[TBL] [Abstract][Full Text] [Related]
17. Improved UNet with Attention for Medical Image Segmentation.
Al Qurri A; Almekkawy M
Sensors (Basel); 2023 Oct; 23(20):. PubMed ID: 37896682
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. 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]
[Next] [New Search]
