149 related articles for article (PubMed ID: 34490103)
1. An Adversarial Deep-Learning-Based Model for Cervical Cancer CTV Segmentation With Multicenter Blinded Randomized Controlled Validation.
Liu Z; Chen W; Guan H; Zhen H; Shen J; Liu X; Liu A; Li R; Geng J; You J; Wang W; Li Z; Zhang Y; Chen Y; Du J; Chen Q; Chen Y; Wang S; Zhang F; Qiu J
Front Oncol; 2021; 11():702270. PubMed ID: 34490103
[TBL] [Abstract][Full Text] [Related]
2. Automatic clinical target volume delineation for cervical cancer in CT images using deep learning.
Shi J; Ding X; Liu X; Li Y; Liang W; Wu J
Med Phys; 2021 Jul; 48(7):3968-3981. PubMed ID: 33905545
[TBL] [Abstract][Full Text] [Related]
3. Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.
Geng J; Zhu X; Liu Z; Chen Q; Bai L; Wang S; Li Y; Wu H; Yue H; Du Y
Radiat Oncol; 2023 Oct; 18(1):164. PubMed ID: 37803462
[TBL] [Abstract][Full Text] [Related]
4. A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy.
Wu Y; Kang K; Han C; Wang S; Chen Q; Chen Y; Zhang F; Liu Z
Cancer Med; 2022 Jan; 11(1):166-175. PubMed ID: 34811957
[TBL] [Abstract][Full Text] [Related]
5. Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy.
Liu Z; Liu X; Guan H; Zhen H; Sun Y; Chen Q; Chen Y; Wang S; Qiu J
Radiother Oncol; 2020 Dec; 153():172-179. PubMed ID: 33039424
[TBL] [Abstract][Full Text] [Related]
6. Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.
Duan J; Bernard M; Downes L; Willows B; Feng X; Mourad WF; St Clair W; Chen Q
Med Phys; 2022 Apr; 49(4):2570-2581. PubMed ID: 35147216
[TBL] [Abstract][Full Text] [Related]
7. Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network.
Liu Z; Liu F; Chen W; Tao Y; Liu X; Zhang F; Shen J; Guan H; Zhen H; Wang S; Chen Q; Chen Y; Hou X
Cancer Manag Res; 2021; 13():8209-8217. PubMed ID: 34754241
[TBL] [Abstract][Full Text] [Related]
8. Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.
Ma CY; Zhou JY; Xu XT; Guo J; Han MF; Gao YZ; Du H; Stahl JN; Maltz JS
J Appl Clin Med Phys; 2022 Feb; 23(2):e13470. PubMed ID: 34807501
[TBL] [Abstract][Full Text] [Related]
9. A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation.
Nie S; Wei Y; Zhao F; Dong Y; Chen Y; Li Q; Du W; Li X; Yang X; Li Z
Radiat Oncol; 2022 Nov; 17(1):182. PubMed ID: 36380378
[TBL] [Abstract][Full Text] [Related]
10. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.
Chung SY; Chang JS; Choi MS; Chang Y; Choi BS; Chun J; Keum KC; Kim JS; Kim YB
Radiat Oncol; 2021 Feb; 16(1):44. PubMed ID: 33632248
[TBL] [Abstract][Full Text] [Related]
11. Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.
Ma CY; Zhou JY; Xu XT; Qin SB; Han MF; Cao XH; Gao YZ; Xu L; Zhou JJ; Zhang W; Jia LC
BMC Med Imaging; 2022 Jul; 22(1):123. PubMed ID: 35810273
[TBL] [Abstract][Full Text] [Related]
12. Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.
Chung SY; Chang JS; Kim YB
Front Oncol; 2023; 13():1119008. PubMed ID: 37188180
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.
Wang Z; Chang Y; Peng Z; Lv Y; Shi W; Wang F; Pei X; Xu XG
J Appl Clin Med Phys; 2020 Dec; 21(12):272-279. PubMed ID: 33238060
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy.
Li Y; Wu W; Sun Y; Yu D; Zhang Y; Wang L; Wang Y; Zhang X; Lu Y
Front Oncol; 2022; 12():945053. PubMed ID: 35982960
[TBL] [Abstract][Full Text] [Related]
16. Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images.
Jiang J; Luo Y; Wang F; Fu Y; Yu H; He Y
Curr Med Imaging; 2021; 17(3):404-409. PubMed ID: 32914716
[TBL] [Abstract][Full Text] [Related]
17. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.
Wong J; Fong A; McVicar N; Smith S; Giambattista J; Wells D; Kolbeck C; Giambattista J; Gondara L; Alexander A
Radiother Oncol; 2020 Mar; 144():152-158. PubMed ID: 31812930
[TBL] [Abstract][Full Text] [Related]
18. A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy.
Balagopal A; Nguyen D; Morgan H; Weng Y; Dohopolski M; Lin MH; Barkousaraie AS; Gonzalez Y; Garant A; Desai N; Hannan R; Jiang S
Med Image Anal; 2021 Aug; 72():102101. PubMed ID: 34111573
[TBL] [Abstract][Full Text] [Related]
19. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers.
Kim N; Chang JS; Kim YB; Kim JS
Radiat Oncol; 2020 May; 15(1):106. PubMed ID: 32404123
[TBL] [Abstract][Full Text] [Related]
20. Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer.
Bakx N; Rijkaart D; van der Sangen M; Theuws J; van der Toorn PP; Verrijssen AS; van der Leer J; Mutsaers J; van Nunen T; Reinders M; Schuengel I; Smits J; Hagelaar E; van Gruijthuijsen D; Bluemink H; Hurkmans C
Tech Innov Patient Support Radiat Oncol; 2023 Jun; 26():100211. PubMed ID: 37229460
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]