610 related articles for article (PubMed ID: 34563895)
21. 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]
22. 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]
23. 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]
24. Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model.
Ashok M; Gupta A
J Comput Biol; 2023 Mar; 30(3):346-362. PubMed ID: 36629856
[TBL] [Abstract][Full Text] [Related]
25. Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.
Tian M; Wang H; Liu X; Ye Y; Ouyang G; Shen Y; Li Z; Wang X; Wu S
Med Phys; 2023 Oct; 50(10):6354-6365. PubMed ID: 37246619
[TBL] [Abstract][Full Text] [Related]
26. Deep learning for head and neck semi-supervised semantic segmentation.
Luan S; Ding Y; Shao J; Zou B; Yu X; Qin N; Zhu B; Wei W; Xue X
Phys Med Biol; 2024 Feb; 69(5):. PubMed ID: 38306968
[No Abstract] [Full Text] [Related]
27. Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images.
Ding Y; Chen Z; Wang Z; Wang X; Hu D; Ma P; Ma C; Wei W; Li X; Xue X; Wang X
J Appl Clin Med Phys; 2022 Apr; 23(4):e13566. PubMed ID: 35192243
[TBL] [Abstract][Full Text] [Related]
28. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.
Jiang J; Hu YC; Tyagi N; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
Med Phys; 2019 Oct; 46(10):4392-4404. PubMed ID: 31274206
[TBL] [Abstract][Full Text] [Related]
29. 3D PET/CT tumor segmentation based on nnU-Net with GCN refinement.
Xue H; Fang Q; Yao Y; Teng Y
Phys Med Biol; 2023 Sep; 68(18):. PubMed ID: 37549672
[No Abstract] [Full Text] [Related]
30. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
Peng Y; Liu Y; Shen G; Chen Z; Chen M; Miao J; Zhao C; Deng J; Qi Z; Deng X
Oral Oncol; 2023 Jan; 136():106261. PubMed ID: 36446186
[TBL] [Abstract][Full Text] [Related]
31. Automated segmentation of lesions and organs at risk on [
Yazdani E; Karamzadeh-Ziarati N; Cheshmi SS; Sadeghi M; Geramifar P; Vosoughi H; Jahromi MK; Kheradpisheh SR
Cancer Imaging; 2024 Feb; 24(1):30. PubMed ID: 38424612
[TBL] [Abstract][Full Text] [Related]
32. Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.
Szentimrey Z; Al-Hayali A; de Ribaupierre S; Fenster A; Ukwatta E
Med Phys; 2024 Jun; ():. PubMed ID: 38857570
[TBL] [Abstract][Full Text] [Related]
33. 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]
34. Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.
Ma J; Nie Z; Wang C; Dong G; Zhu Q; He J; Gui L; Yang X
Phys Med Biol; 2020 Dec; 65(22):225034. PubMed ID: 33045699
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network.
Zhang F; Wang Q; Yang A; Lu N; Jiang H; Chen D; Yu Y; Wang Y
Front Oncol; 2022; 12():861857. PubMed ID: 35371991
[TBL] [Abstract][Full Text] [Related]
37. U-net architecture with embedded Inception-ResNet-v2 image encoding modules for automatic segmentation of organs-at-risk in head and neck cancer radiation therapy based on computed tomography scans.
Siciarz P; McCurdy B
Phys Med Biol; 2022 Jun; 67(11):. PubMed ID: 35134792
[No Abstract] [Full Text] [Related]
38. 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]
39. Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.
Ribeiro MF; Marschner S; Kawula M; Rabe M; Corradini S; Belka C; Riboldi M; Landry G; Kurz C
Radiat Oncol; 2023 Aug; 18(1):135. PubMed ID: 37574549
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]
