4500 related articles for article (PubMed ID: 30136285)
21. 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]
22. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.
Fritscher KD; Peroni M; Zaffino P; Spadea MF; Schubert R; Sharp G
Med Phys; 2014 May; 41(5):051910. PubMed ID: 24784389
[TBL] [Abstract][Full Text] [Related]
23. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.
Thomson D; Boylan C; Liptrot T; Aitkenhead A; Lee L; Yap B; Sykes A; Rowbottom C; Slevin N
Radiat Oncol; 2014 Aug; 9():173. PubMed ID: 25086641
[TBL] [Abstract][Full Text] [Related]
24. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy.
Dai X; Lei Y; Wang T; Dhabaan AH; McDonald M; Beitler JJ; Curran WJ; Zhou J; Liu T; Yang X
Phys Med Biol; 2021 Feb; 66(4):045021. PubMed ID: 33412527
[TBL] [Abstract][Full Text] [Related]
25. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.
Ren X; Xiang L; Nie D; Shao Y; Zhang H; Shen D; Wang Q
Med Phys; 2018 May; 45(5):2063-2075. PubMed ID: 29480928
[TBL] [Abstract][Full Text] [Related]
26. Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.
Feng X; Qing K; Tustison NJ; Meyer CH; Chen Q
Med Phys; 2019 May; 46(5):2169-2180. PubMed ID: 30830685
[TBL] [Abstract][Full Text] [Related]
27. Automatic multiorgan segmentation in thorax CT images using U-net-GAN.
Dong X; Lei Y; Wang T; Thomas M; Tang L; Curran WJ; Liu T; Yang X
Med Phys; 2019 May; 46(5):2157-2168. PubMed ID: 30810231
[TBL] [Abstract][Full Text] [Related]
28. Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.
Shen G; Jin X; Sun C; Li Q
Front Public Health; 2022; 10():813135. PubMed ID: 35493368
[TBL] [Abstract][Full Text] [Related]
29. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.
Hu P; Wu F; Peng J; Bao Y; Chen F; Kong D
Int J Comput Assist Radiol Surg; 2017 Mar; 12(3):399-411. PubMed ID: 27885540
[TBL] [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. 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]
32. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
[TBL] [Abstract][Full Text] [Related]
33. Multi-View Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at Risk in Head and Neck CT Images.
Liang S; Thung KH; Nie D; Zhang Y; Shen D
IEEE Trans Med Imaging; 2020 Sep; 39(9):2794-2805. PubMed ID: 32091997
[TBL] [Abstract][Full Text] [Related]
34. General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.
Amjad A; Xu J; Thill D; Lawton C; Hall W; Awan MJ; Shukla M; Erickson BA; Li XA
Med Phys; 2022 Mar; 49(3):1686-1700. PubMed ID: 35094390
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Dynamic multiatlas selection-based consensus segmentation of head and neck structures from CT images.
Haq R; Berry SL; Deasy JO; Hunt M; Veeraraghavan H
Med Phys; 2019 Dec; 46(12):5612-5622. PubMed ID: 31587300
[TBL] [Abstract][Full Text] [Related]
37. Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy.
Sun Y; Shi H; Zhang S; Wang P; Zhao W; Zhou X; Yuan K
Med Phys; 2019 May; 46(5):2214-2222. PubMed ID: 30815885
[TBL] [Abstract][Full Text] [Related]
38. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling.
Fortunati V; Verhaart RF; van der Lijn F; Niessen WJ; Veenland JF; Paulides MM; van Walsum T
Med Phys; 2013 Jul; 40(7):071905. PubMed ID: 23822442
[TBL] [Abstract][Full Text] [Related]
39. Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.
Kieselmann JP; Kamerling CP; Burgos N; Menten MJ; Fuller CD; Nill S; Cardoso MJ; Oelfke U
Phys Med Biol; 2018 Jul; 63(14):145007. PubMed ID: 29882749
[TBL] [Abstract][Full Text] [Related]
40. Fully automated 3D segmentation and separation of multiple cervical vertebrae in CT images using a 2D convolutional neural network.
Bae HJ; Hyun H; Byeon Y; Shin K; Cho Y; Song YJ; Yi S; Kuh SU; Yeom JS; Kim N
Comput Methods Programs Biomed; 2020 Feb; 184():105119. PubMed ID: 31627152
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
