173 related articles for article (PubMed ID: 36273078)
1. A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation.
Zhu R; Oda M; Hayashi Y; Kitasaka T; Misawa K; Fujiwara M; Mori K
Int J Comput Assist Radiol Surg; 2023 Mar; 18(3):461-472. PubMed ID: 36273078
[TBL] [Abstract][Full Text] [Related]
2. Abdominal artery segmentation method from CT volumes using fully convolutional neural network.
Oda M; Roth HR; Kitasaka T; Misawa K; Fujiwara M; Mori K
Int J Comput Assist Radiol Surg; 2019 Dec; 14(12):2069-2081. PubMed ID: 31493112
[TBL] [Abstract][Full Text] [Related]
3. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks.
Chen CI; Lu NH; Huang YH; Liu KY; Hsu SY; Matsushima A; Wang YM; Chen TB
J Xray Sci Technol; 2022; 30(5):953-966. PubMed ID: 35754254
[TBL] [Abstract][Full Text] [Related]
4. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
Zhou X
Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
[TBL] [Abstract][Full Text] [Related]
5. A coronary artery CTA segmentation approach based on deep learning.
Huang C; Yin C
J Xray Sci Technol; 2022; 30(2):245-259. PubMed ID: 34957947
[TBL] [Abstract][Full Text] [Related]
6. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.
Zhou X; Takayama R; Wang S; Hara T; Fujita H
Med Phys; 2017 Oct; 44(10):5221-5233. PubMed ID: 28730602
[TBL] [Abstract][Full Text] [Related]
7. Vessel segmentation from volumetric images: a multi-scale double-pathway network with class-balanced loss at the voxel level.
Chen Y; Fan S; Chen Y; Che C; Cao X; He X; Song X; Zhao F
Med Phys; 2021 Jul; 48(7):3804-3814. PubMed ID: 33969487
[TBL] [Abstract][Full Text] [Related]
8. High-resolution 3D abdominal segmentation with random patch network fusion.
Tang Y; Gao R; Lee HH; Han S; Chen Y; Gao D; Nath V; Bermudez C; Savona MR; Abramson RG; Bao S; Lyu I; Huo Y; Landman BA
Med Image Anal; 2021 Apr; 69():101894. PubMed ID: 33421919
[TBL] [Abstract][Full Text] [Related]
9. An application of cascaded 3D fully convolutional networks for medical image segmentation.
Roth HR; Oda H; Zhou X; Shimizu N; Yang Y; Hayashi Y; Oda M; Fujiwara M; Misawa K; Mori K
Comput Med Imaging Graph; 2018 Jun; 66():90-99. PubMed ID: 29573583
[TBL] [Abstract][Full Text] [Related]
10. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
[TBL] [Abstract][Full Text] [Related]
11. Complete abdomen and pelvis segmentation using U-net variant architecture.
Weston AD; Korfiatis P; Philbrick KA; Conte GM; Kostandy P; Sakinis T; Zeinoddini A; Boonrod A; Moynagh M; Takahashi N; Erickson BJ
Med Phys; 2020 Nov; 47(11):5609-5618. PubMed ID: 32740931
[TBL] [Abstract][Full Text] [Related]
12. Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images.
Gu L; Cai XC
Artif Intell Med; 2021 Nov; 121():102189. PubMed ID: 34763804
[TBL] [Abstract][Full Text] [Related]
13. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
14. Automatic multiorgan segmentation in thorax CT images using U-net-GAN.
Dong X; Lei Y; Wang T; Thomas M; Tang L; Curran WJ; Liu T; Yang X
Med Phys; 2019 May; 46(5):2157-2168. PubMed ID: 30810231
[TBL] [Abstract][Full Text] [Related]
15. PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans.
Nikan S; Van Osch K; Bartling M; Allen DG; Rohani SA; Connors B; Agrawal SK; Ladak HM
IEEE Trans Image Process; 2021; 30():739-753. PubMed ID: 33226942
[TBL] [Abstract][Full Text] [Related]
16. Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation.
Zhao Q; Zhong L; Xiao J; Zhang J; Chen Y; Liao W; Zhang S; Wang G
IEEE Trans Med Imaging; 2023 Sep; 42(9):2513-2523. PubMed ID: 37030798
[TBL] [Abstract][Full Text] [Related]
17. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.
Wang B; Lei Y; Tian S; Wang T; Liu Y; Patel P; Jani AB; Mao H; Curran WJ; Liu T; Yang X
Med Phys; 2019 Apr; 46(4):1707-1718. PubMed ID: 30702759
[TBL] [Abstract][Full Text] [Related]
18. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.
Men K; Dai J; Li Y
Med Phys; 2017 Dec; 44(12):6377-6389. PubMed ID: 28963779
[TBL] [Abstract][Full Text] [Related]
19. RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.
Liu J; Wang C; Guo J; Shao J; Xu X; Liu X; Li H; Li W; Yi Z
Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):895-904. PubMed ID: 33846890
[TBL] [Abstract][Full Text] [Related]
20. Convolutional Neural Network Ensemble Segmentation With Ratio-Based Sampling for the Arteries and Veins in Abdominal CT Scans.
Golla AK; Bauer DF; Schmidt R; Russ T; Norenberg D; Chung K; Tonnes C; Schad LR; Zollner FG
IEEE Trans Biomed Eng; 2021 May; 68(5):1518-1526. PubMed ID: 33275574
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
