151 related articles for article (PubMed ID: 35598077)
21. Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.
Wang T; Lei Y; Roper J; Ghavidel B; Beitler JJ; McDonald M; Curran WJ; Liu T; Yang X
Phys Med Biol; 2021 May; 66(11):. PubMed ID: 33915524
[TBL] [Abstract][Full Text] [Related]
22. Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network.
Dai X; Lei Y; Wang T; Zhou J; Roper J; McDonald M; Beitler JJ; Curran WJ; Liu T; Yang X
Med Phys; 2021 Oct; 48(10):5862-5873. PubMed ID: 34342878
[TBL] [Abstract][Full Text] [Related]
23. ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.
Zhang Z; Zhao T; Gay H; Zhang W; Sun B
Med Phys; 2021 Jan; 48(1):227-237. PubMed ID: 33151620
[TBL] [Abstract][Full Text] [Related]
24. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.
Wang Y; Zhou Y; Shen W; Park S; Fishman EK; Yuille AL
Med Image Anal; 2019 Jul; 55():88-102. PubMed ID: 31035060
[TBL] [Abstract][Full Text] [Related]
25. Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation.
Jiang J; Hu YC; Tyagi N; Wang C; Lee N; Deasy JO; Sean B; Veeraraghavan H
Phys Med Biol; 2020 Oct; 65(20):205001. PubMed ID: 33027063
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. 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]
28. 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]
29. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.
Qazi AA; Pekar V; Kim J; Xie J; Breen SL; Jaffray DA
Med Phys; 2011 Nov; 38(11):6160-70. PubMed ID: 22047381
[TBL] [Abstract][Full Text] [Related]
30. MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.
Li W; Qin S; Li F; Wang L
Med Phys; 2021 Jan; 48(1):329-341. PubMed ID: 33222222
[TBL] [Abstract][Full Text] [Related]
31. Complete abdomen and pelvis segmentation using U-net variant architecture.
Weston AD; Korfiatis P; Philbrick KA; Conte GM; Kostandy P; Sakinis T; Zeinoddini A; Boonrod A; Moynagh M; Takahashi N; Erickson BJ
Med Phys; 2020 Nov; 47(11):5609-5618. PubMed ID: 32740931
[TBL] [Abstract][Full Text] [Related]
32. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
Zhang D; Yang Z; Jiang S; Zhou Z; Meng M; Wang W
J Appl Clin Med Phys; 2020 Oct; 21(10):158-169. PubMed ID: 32991783
[TBL] [Abstract][Full Text] [Related]
33. The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.
Wang J; Wang S; Liang W; Zhang N; Zhang Y
J Appl Clin Med Phys; 2022 May; 23(5):e13597. PubMed ID: 35363415
[TBL] [Abstract][Full Text] [Related]
34. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.
Sultana S; Robinson A; Song DY; Lee J
J Med Imaging (Bellingham); 2020 Sep; 7(5):055001. PubMed ID: 33102622
[No Abstract] [Full Text] [Related]
35. A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans.
Chen Y; Zheng C; Zhou T; Feng L; Liu L; Zeng Q; Wang G
Comput Biol Med; 2023 Jan; 152():106421. PubMed ID: 36527780
[TBL] [Abstract][Full Text] [Related]
36. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
[TBL] [Abstract][Full Text] [Related]
37. Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.
Jiang J; Riyahi Alam S; Chen I; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
Med Phys; 2021 Jul; 48(7):3702-3713. PubMed ID: 33905558
[TBL] [Abstract][Full Text] [Related]
38. Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.
Iyer A; Thor M; Onochie I; Hesse J; Zakeri K; LoCastro E; Jiang J; Veeraraghavan H; Elguindi S; Lee NY; Deasy JO; Apte AP
Phys Med Biol; 2022 Jan; 67(2):. PubMed ID: 34874302
[No Abstract] [Full Text] [Related]
39. 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]
40. Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.
Han X; Hong J; Reyngold M; Crane C; Cuaron J; Hajj C; Mann J; Zinovoy M; Greer H; Yorke E; Mageras G; Niethammer M
Med Phys; 2021 Jun; 48(6):3084-3095. PubMed ID: 33905539
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]