336 related articles for article (PubMed ID: 29361340)
1. Multiple supervised residual network for osteosarcoma segmentation in CT images.
Zhang R; Huang L; Xia W; Zhang B; Qiu B; Gao X
Comput Med Imaging Graph; 2018 Jan; 63():1-8. PubMed ID: 29361340
[TBL] [Abstract][Full Text] [Related]
2. MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images.
Huang L; Xia W; Zhang B; Qiu B; Gao X
Comput Methods Programs Biomed; 2017 May; 143():67-74. PubMed ID: 28391820
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.
Qin Y; Zheng H; Huang X; Yang J; Zhu YM
Med Phys; 2019 Mar; 46(3):1218-1229. PubMed ID: 30575046
[TBL] [Abstract][Full Text] [Related]
5. Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.
Chen J; Li Y; Luna LP; Chung HW; Rowe SP; Du Y; Solnes LB; Frey EC
Med Phys; 2021 Jul; 48(7):3860-3877. PubMed ID: 33905560
[TBL] [Abstract][Full Text] [Related]
6. Full convolutional network based multiple side-output fusion architecture for the segmentation of rectal tumors in magnetic resonance images: A multi-vendor study.
Wang M; Xie P; Ran Z; Jian J; Zhang R; Xia W; Yu T; Ni C; Gu J; Gao X; Meng X
Med Phys; 2019 Jun; 46(6):2659-2668. PubMed ID: 30972763
[TBL] [Abstract][Full Text] [Related]
7. Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.
Ouhmich F; Agnus V; Noblet V; Heitz F; Pessaux P
Int J Comput Assist Radiol Surg; 2019 Aug; 14(8):1275-1284. PubMed ID: 31041697
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images.
Huang Z; Guo Y; Zhang N; Huang X; Decazes P; Becker S; Ruan S
Comput Biol Med; 2022 Dec; 151(Pt A):106230. PubMed ID: 36306574
[TBL] [Abstract][Full Text] [Related]
10. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.
Hu Y; Guo Y; Wang Y; Yu J; Li J; Zhou S; Chang C
Med Phys; 2019 Jan; 46(1):215-228. PubMed ID: 30374980
[TBL] [Abstract][Full Text] [Related]
11. Incorporation of residual attention modules into two neural networks for low-dose CT denoising.
Li M; Du Q; Duan L; Yang X; Zheng J; Jiang H; Li M
Med Phys; 2021 Jun; 48(6):2973-2990. PubMed ID: 33890681
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.
Zhu J; Zhang J; Qiu B; Liu Y; Liu X; Chen L
Acta Oncol; 2019 Feb; 58(2):257-264. PubMed ID: 30398090
[TBL] [Abstract][Full Text] [Related]
14. Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery.
Wang T; Lei Y; Tian S; Jiang X; Zhou J; Liu T; Dresser S; Curran WJ; Shu HK; Yang X
Med Phys; 2019 Jul; 46(7):3133-3141. PubMed ID: 31050804
[TBL] [Abstract][Full Text] [Related]
15. MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.
Wu Y; Jiang H; Pang W
Comput Biol Med; 2023 May; 158():106818. PubMed ID: 36966557
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
Ibragimov B; Xing L
Med Phys; 2017 Feb; 44(2):547-557. PubMed ID: 28205307
[TBL] [Abstract][Full Text] [Related]
18. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.
Heutink F; Koch V; Verbist B; van der Woude WJ; Mylanus E; Huinck W; Sechopoulos I; Caballo M
Comput Methods Programs Biomed; 2020 Jul; 191():105387. PubMed ID: 32109685
[TBL] [Abstract][Full Text] [Related]
19. A 3D segmentation network of mandible from CT scan with combination of multiple convolutional modules and edge supervision in mandibular reconstruction.
Xu J; Liu J; Zhang D; Zhou Z; Zhang C; Chen X
Comput Biol Med; 2021 Nov; 138():104925. PubMed ID: 34656866
[TBL] [Abstract][Full Text] [Related]
20. Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation.
Chen H; Yan S; Xie M; Ye Y; Ye Y; Zhu D; Su L; Huang J
Comput Methods Programs Biomed; 2022 Mar; 215():106608. PubMed ID: 35063713
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
