444 related articles for article (PubMed ID: 35897425)
1. 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]
2. 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]
3. Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.
Alzahrani N; Henry A; Clark A; Murray L; Nix M; Al-Qaisieh B
Phys Med Biol; 2023 Aug; 68(17):. PubMed ID: 37579753
[No Abstract] [Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. 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]
8. Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.
Gibbons E; Hoffmann M; Westhuyzen J; Hodgson A; Chick B; Last A
J Med Radiat Sci; 2023 Apr; 70 Suppl 2(Suppl 2):15-25. PubMed ID: 36148621
[TBL] [Abstract][Full Text] [Related]
9. A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases.
Zhong Y; Yang Y; Fang Y; Wang J; Hu W
Front Oncol; 2021; 11():638197. PubMed ID: 34026615
[TBL] [Abstract][Full Text] [Related]
10. Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.
Vrtovec T; Močnik D; Strojan P; Pernuš F; Ibragimov B
Med Phys; 2020 Sep; 47(9):e929-e950. PubMed ID: 32510603
[TBL] [Abstract][Full Text] [Related]
11. Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.
Wong J; Huang V; Wells D; Giambattista J; Giambattista J; Kolbeck C; Otto K; Saibishkumar EP; Alexander A
Radiat Oncol; 2021 Jun; 16(1):101. PubMed ID: 34103062
[TBL] [Abstract][Full Text] [Related]
12. AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.
Wu X; Udupa JK; Tong Y; Odhner D; Pednekar GV; Simone CB; McLaughlin D; Apinorasethkul C; Apinorasethkul O; Lukens J; Mihailidis D; Shammo G; James P; Tiwari A; Wojtowicz L; Camaratta J; Torigian DA
Med Image Anal; 2019 May; 54():45-62. PubMed ID: 30831357
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.
Hague C; McPartlin A; Lee LW; Hughes C; Mullan D; Beasley W; Green A; Price G; Whitehurst P; Slevin N; van Herk M; West C; Chuter R
Radiother Oncol; 2021 May; 158():112-117. PubMed ID: 33636229
[TBL] [Abstract][Full Text] [Related]
15. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.
Guo H; Wang J; Xia X; Zhong Y; Peng J; Zhang Z; Hu W
Radiat Oncol; 2021 Jun; 16(1):113. PubMed ID: 34162410
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.
Temple SWP; Rowbottom CG
J Appl Clin Med Phys; 2024 Jun; 25(6):e14273. PubMed ID: 38263866
[TBL] [Abstract][Full Text] [Related]
18. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.
van Dijk LV; Van den Bosch L; Aljabar P; Peressutti D; Both S; J H M Steenbakkers R; Langendijk JA; Gooding MJ; Brouwer CL
Radiother Oncol; 2020 Jan; 142():115-123. PubMed ID: 31653573
[TBL] [Abstract][Full Text] [Related]
19. Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy.
Maduro Bustos LA; Sarkar A; Doyle LA; Andreou K; Noonan J; Nurbagandova D; Shah SA; Irabor OC; Mourtada F
J Appl Clin Med Phys; 2023 Nov; 24(11):e14090. PubMed ID: 37464581
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.
Urago Y; Okamoto H; Kaneda T; Murakami N; Kashihara T; Takemori M; Nakayama H; Iijima K; Chiba T; Kuwahara J; Katsuta S; Nakamura S; Chang W; Saitoh H; Igaki H
Radiat Oncol; 2021 Sep; 16(1):175. PubMed ID: 34503533
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
