122 related articles for article (PubMed ID: 37693394)
1. Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation.
Wahid KA; Sahin O; Kundu S; Lin D; Alanis A; Tehami S; Kamel S; Duke S; Sherer MV; Rasmussen M; Korreman S; Fuentes D; Cislo M; Nelms BE; Christodouleas JP; Murphy JD; Mohamed ASR; He R; Naser MA; Gillespie EF; Fuller CD
medRxiv; 2023 Sep; ():. PubMed ID: 37693394
[TBL] [Abstract][Full Text] [Related]
2. Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.
Wahid KA; Sahin O; Kundu S; Lin D; Alanis A; Tehami S; Kamel S; Duke S; Sherer MV; Rasmussen M; Korreman S; Fuentes D; Cislo M; Nelms BE; Christodouleas JP; Murphy JD; Mohamed ASR; He R; Naser MA; Gillespie EF; Fuller CD
JCO Clin Cancer Inform; 2024 Jun; 8():e2300174. PubMed ID: 38870441
[TBL] [Abstract][Full Text] [Related]
3.
Lin D; Wahid KA; Nelms BE; He R; Naser MA; Duke S; Sherer MV; Christodouleas JP; Mohamed ASR; Cislo M; Murphy JD; Fuller CD; Gillespie EF
J Med Imaging (Bellingham); 2023 Feb; 10(Suppl 1):S11903. PubMed ID: 36761036
[TBL] [Abstract][Full Text] [Related]
4. Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.
Sahlsten J; Jaskari J; Wahid KA; Ahmed S; Glerean E; He R; Kann BH; Mäkitie A; Fuller CD; Naser MA; Kaski K
medRxiv; 2023 Feb; ():. PubMed ID: 36865296
[TBL] [Abstract][Full Text] [Related]
5. Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets.
Chlap P; Min H; Dowling J; Field M; Cloak K; Leong T; Lee M; Chu J; Tan J; Tran P; Kron T; Sidhom M; Wiltshire K; Keats S; Kneebone A; Haworth A; Ebert MA; Vinod SK; Holloway L
Comput Med Imaging Graph; 2024 Jun; 116():102403. PubMed ID: 38878632
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.
Podobnik G; Ibragimov B; Peterlin P; Strojan P; Vrtovec T
Med Phys; 2024 Mar; 51(3):2175-2186. PubMed ID: 38230752
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.
Korte JC; Hardcastle N; Ng SP; Clark B; Kron T; Jackson P
Med Phys; 2021 Dec; 48(12):7757-7772. PubMed ID: 34676555
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.
Choi MS; Chang JS; Kim K; Kim JH; Kim TH; Kim S; Cha H; Cho O; Choi JH; Kim M; Kim J; Kim TG; Yeo SG; Chang AR; Ahn SJ; Choi J; Kang KM; Kwon J; Koo T; Kim MY; Choi SH; Jeong BK; Jang BS; Jo IY; Lee H; Kim N; Park HJ; Im JH; Lee SW; Cho Y; Lee SY; Chang JH; Chun J; Lee EM; Kim JS; Shin KH; Kim YB
Breast; 2024 Feb; 73():103599. PubMed ID: 37992527
[TBL] [Abstract][Full Text] [Related]
15. The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.
Poel R; Rüfenacht E; Hermann E; Scheib S; Manser P; Aebersold DM; Reyes M
Med Image Anal; 2021 Oct; 73():102161. PubMed ID: 34293536
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.
Zhao Y; Wang X; Phan J; Chen X; Lee A; Yu C; Huang K; Court LE; Pan T; Wang H; Wahid KA; Mohamed ASR; Naser M; Fuller CD; Yang J
Med Phys; 2024 Jun; ():. PubMed ID: 38896829
[TBL] [Abstract][Full Text] [Related]
18. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.
Oktay O; Nanavati J; Schwaighofer A; Carter D; Bristow M; Tanno R; Jena R; Barnett G; Noble D; Rimmer Y; Glocker B; O'Hara K; Bishop C; Alvarez-Valle J; Nori A
JAMA Netw Open; 2020 Nov; 3(11):e2027426. PubMed ID: 33252691
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.
Kawula M; Hadi I; Nierer L; Vagni M; Cusumano D; Boldrini L; Placidi L; Corradini S; Belka C; Landry G; Kurz C
Med Phys; 2023 Mar; 50(3):1573-1585. PubMed ID: 36259384
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
