190 related articles for article (PubMed ID: 35905660)
1. TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner.
Huang Z; Tang S; Chen Z; Wang G; Shen H; Zhou Y; Wang H; Fan W; Liang D; Hu Y; Hu Z
Comput Biol Med; 2022 Sep; 148():105869. PubMed ID: 35905660
[TBL] [Abstract][Full Text] [Related]
2. Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.
Zhao L; Lu Z; Jiang J; Zhou Y; Wu Y; Feng Q
J Digit Imaging; 2019 Jun; 32(3):462-470. PubMed ID: 30719587
[TBL] [Abstract][Full Text] [Related]
3. SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.
Li GY; Chen J; Jang SI; Gong K; Li Q
Med Phys; 2024 Mar; 51(3):2096-2107. PubMed ID: 37776263
[TBL] [Abstract][Full Text] [Related]
4. WET-UNet: Wavelet integrated efficient transformer networks for nasopharyngeal carcinoma tumor segmentation.
Zeng Y; Li J; Zhao Z; Liang W; Zeng P; Shen S; Zhang K; Shen C
Sci Prog; 2024; 107(2):368504241232537. PubMed ID: 38567422
[TBL] [Abstract][Full Text] [Related]
5. Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images.
Qi Y; Li J; Chen H; Guo Y; Yin Y; Gong G; Wang L
Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):871-882. PubMed ID: 33782844
[TBL] [Abstract][Full Text] [Related]
6. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.
Tie X; Lam SK; Zhang Y; Lee KH; Au KH; Cai J
Med Phys; 2020 Apr; 47(4):1750-1762. PubMed ID: 32012292
[TBL] [Abstract][Full Text] [Related]
7. [Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network].
Yang X; Li X; Zhang X; Song F; Huang S; Xia Y
Nan Fang Yi Ke Da Xue Xue Bao; 2020 Nov; 40(11):1579-1586. PubMed ID: 33243744
[TBL] [Abstract][Full Text] [Related]
8. A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma.
Chen X; Yang B; Li J; Zhu J; Ma X; Chen D; Hu Z; Men K; Dai J
Phys Med Biol; 2021 Nov; 66(22):. PubMed ID: 34700300
[No Abstract] [Full Text] [Related]
9. 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]
10. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning.
Peng Y; Chen S; Qin A; Chen M; Gao X; Liu Y; Miao J; Gu H; Zhao C; Deng X; Qi Z
Radiother Oncol; 2020 Sep; 150():217-224. PubMed ID: 32622781
[TBL] [Abstract][Full Text] [Related]
11. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. The valuable role of dynamic
Huang X; Zhuang M; Yang S; Wang Y; Liu Q; Xu X; Xiao M; Peng Y; Jiang P; Xu W; Guo S; Wang R; Wei W; Zhong G; Zhou Y; Peng S; Li X; Cui J; Wang S; Zhang Y; Liu Z
Radiother Oncol; 2023 Feb; 179():109440. PubMed ID: 36566989
[TBL] [Abstract][Full Text] [Related]
14. Nasopharyngeal carcinoma segmentation using a region growing technique.
Chanapai W; Bhongmakapat T; Tuntiyatorn L; Ritthipravat P
Int J Comput Assist Radiol Surg; 2012 May; 7(3):413-22. PubMed ID: 21671094
[TBL] [Abstract][Full Text] [Related]
15. Cross modality fusion for modality-specific lung tumor segmentation in PET-CT images.
Zhang X; Zhang B; Deng S; Meng Q; Chen X; Xiang D
Phys Med Biol; 2022 Nov; 67(22):. PubMed ID: 36220014
[TBL] [Abstract][Full Text] [Related]
16. DMCT-Net: dual modules convolution transformer network for head and neck tumor segmentation in PET/CT.
Wang J; Peng Y; Guo Y
Phys Med Biol; 2023 May; 68(11):. PubMed ID: 37141902
[No Abstract] [Full Text] [Related]
17. Automatic segmentation of organs-at-risks of nasopharynx cancer and lung cancer by cross-layer attention fusion network with TELD-Loss.
Liu Z; Sun C; Wang H; Li Z; Gao Y; Lei W; Zhang S; Wang G; Zhang S
Med Phys; 2021 Nov; 48(11):6987-7002. PubMed ID: 34608652
[TBL] [Abstract][Full Text] [Related]
18. Metabolic Anomaly Appearance Aware U-Net for Automatic Lymphoma Segmentation in Whole-Body PET/CT Scans.
Shi T; Jiang H; Wang M; Diao Z; Zhang G; Yao YD
IEEE J Biomed Health Inform; 2023 May; 27(5):2465-2476. PubMed ID: 37027631
[TBL] [Abstract][Full Text] [Related]
19. The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.
Li S; Xiao J; He L; Peng X; Yuan X
Technol Cancer Res Treat; 2019; 18():1533033819884561. PubMed ID: 31736433
[TBL] [Abstract][Full Text] [Related]
20. Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.
Huang Z; Wu Y; Fu F; Meng N; Gu F; Wu Q; Zhou Y; Yang Y; Liu X; Zheng H; Liang D; Wang M; Hu Z
Eur J Nucl Med Mol Imaging; 2022 Jul; 49(8):2482-2492. PubMed ID: 35312030
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
