4416 related articles for article (PubMed ID: 30136285)
1. 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]
2. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
Tong N; Gou S; Yang S; Cao M; Sheng K
Med Phys; 2019 Jun; 46(6):2669-2682. PubMed ID: 31002188
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.
Gou S; Tong N; Qi S; Yang S; Chin R; Sheng K
Phys Med Biol; 2020 Dec; 65(24):245034. PubMed ID: 32097892
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.
Zhang Z; Zhao T; Gay H; Zhang W; Sun B
Med Phys; 2021 Nov; 48(11):7052-7062. PubMed ID: 34655077
[TBL] [Abstract][Full Text] [Related]
8. Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer.
Hoang Duc AK; Eminowicz G; Mendes R; Wong SL; McClelland J; Modat M; Cardoso MJ; Mendelson AF; Veiga C; Kadir T; D'Souza D; Ourselin S
Med Phys; 2015 Sep; 42(9):5027-34. PubMed ID: 26328953
[TBL] [Abstract][Full Text] [Related]
9. Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network.
Dai X; Lei Y; Wang T; Zhou J; Roper J; McDonald M; Beitler JJ; Curran WJ; Liu T; Yang X
Med Phys; 2021 Oct; 48(10):5862-5873. PubMed ID: 34342878
[TBL] [Abstract][Full Text] [Related]
10. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.
Qazi AA; Pekar V; Kim J; Xie J; Breen SL; Jaffray DA
Med Phys; 2011 Nov; 38(11):6160-70. PubMed ID: 22047381
[TBL] [Abstract][Full Text] [Related]
11. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI.
Liu Y; Lei Y; Fu Y; Wang T; Zhou J; Jiang X; McDonald M; Beitler JJ; Curran WJ; Liu T; Yang X
Med Phys; 2020 Sep; 47(9):4294-4302. PubMed ID: 32648602
[TBL] [Abstract][Full Text] [Related]
12. Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.
Wang T; Lei Y; Roper J; Ghavidel B; Beitler JJ; McDonald M; Curran WJ; Liu T; Yang X
Phys Med Biol; 2021 May; 66(11):. PubMed ID: 33915524
[TBL] [Abstract][Full Text] [Related]
13. Abdomen CT multi-organ segmentation using token-based MLP-Mixer.
Pan S; Chang CW; Wang T; Wynne J; Hu M; Lei Y; Liu T; Patel P; Roper J; Yang X
Med Phys; 2023 May; 50(5):3027-3038. PubMed ID: 36463516
[TBL] [Abstract][Full Text] [Related]
14. Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.
Liu P; Sun Y; Zhao X; Yan Y
Biomed Eng Online; 2023 Nov; 22(1):104. PubMed ID: 37915046
[TBL] [Abstract][Full Text] [Related]
15. Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.
D'Aviero A; Re A; Catucci F; Piccari D; Votta C; Piro D; Piras A; Di Dio C; Iezzi M; Preziosi F; Menna S; Quaranta F; Boschetti A; Marras M; Miccichè F; Gallus R; Indovina L; Bussu F; Valentini V; Cusumano D; Mattiucci GC
Int J Environ Res Public Health; 2022 Jul; 19(15):. PubMed ID: 35897425
[TBL] [Abstract][Full Text] [Related]
16. Transfer learning for auto-segmentation of 17 organs-at-risk in the head and neck: Bridging the gap between institutional and public datasets.
Clark B; Hardcastle N; Johnston LA; Korte J
Med Phys; 2024 Feb; ():. PubMed ID: 38376454
[TBL] [Abstract][Full Text] [Related]
17. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.
Ahn SH; Yeo AU; Kim KH; Kim C; Goh Y; Cho S; Lee SB; Lim YK; Kim H; Shin D; Kim T; Kim TH; Youn SH; Oh ES; Jeong JH
Radiat Oncol; 2019 Nov; 14(1):213. PubMed ID: 31775825
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Self-paced DenseNet with boundary constraint for automated multi-organ segmentation on abdominal CT images.
Tong N; Gou S; Niu T; Yang S; Sheng K
Phys Med Biol; 2020 Jul; 65(13):135011. PubMed ID: 32657281
[TBL] [Abstract][Full Text] [Related]
20. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.
Liu Z; Liu X; Xiao B; Wang S; Miao Z; Sun Y; Zhang F
Phys Med; 2020 Jan; 69():184-191. PubMed ID: 31918371
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]