These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

126 related articles for article (PubMed ID: 36233460)

  • 1. Deep Learning for the Automatic Segmentation of Extracranial Venous Malformations of the Head and Neck from MR Images Using 3D U-Net.
    Ryu JY; Hong HK; Cho HG; Lee JS; Yoo BC; Choi MH; Chung HY
    J Clin Med; 2022 Sep; 11(19):. PubMed ID: 36233460
    [TBL] [Abstract][Full Text] [Related]  

  • 2. [Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network].
    Dai X; Wang X; Du L; Ma N; Xu S; Cai B; Wang S; Wang Z; Qu B
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2020 Feb; 37(1):136-141. PubMed ID: 32096387
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles.
    Wang XR; Ma X; Jin LX; Gao YJ; Xue YJ; Li JL; Bai WX; Han MF; Zhou Q; Shi F; Wang J
    Front Neuroinform; 2022; 16():937891. PubMed ID: 36120083
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.
    Orlando N; Gillies DJ; Gyacskov I; Romagnoli C; D'Souza D; Fenster A
    Med Phys; 2020 Jun; 47(6):2413-2426. PubMed ID: 32166768
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.
    Fashandi H; Kuling G; Lu Y; Wu H; Martel AL
    Med Phys; 2019 Mar; 46(3):1230-1244. PubMed ID: 30609062
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.
    Ushinsky A; Bardis M; Glavis-Bloom J; Uchio E; Chantaduly C; Nguyentat M; Chow D; Chang PD; Houshyar R
    AJR Am J Roentgenol; 2021 Jan; 216(1):111-116. PubMed ID: 32812797
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.
    Zhu HT; Zhang XY; Shi YJ; Li XT; Sun YS
    J Appl Clin Med Phys; 2021 Sep; 22(9):324-331. PubMed ID: 34343402
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System.
    Schelb P; Tavakoli AA; Tubtawee T; Hielscher T; Radtke JP; Görtz M; Schütz V; Kuder TA; Schimmöller L; Stenzinger A; Hohenfellner M; Schlemmer HP; Bonekamp D
    Rofo; 2021 May; 193(5):559-573. PubMed ID: 33212541
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.
    Kawahara D; Tsuneda M; Ozawa S; Okamoto H; Nakamura M; Nishio T; Nagata Y
    J Appl Clin Med Phys; 2022 May; 23(5):e13579. PubMed ID: 35263027
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
    Lin YC; Lin CH; Lu HY; Chiang HJ; Wang HK; Huang YT; Ng SH; Hong JH; Yen TC; Lai CH; Lin G
    Eur Radiol; 2020 Mar; 30(3):1297-1305. PubMed ID: 31712961
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry.
    Norman B; Pedoia V; Majumdar S
    Radiology; 2018 Jul; 288(1):177-185. PubMed ID: 29584598
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
    Zhang Y; Chen JH; Chang KT; Park VY; Kim MJ; Chan S; Chang P; Chow D; Luk A; Kwong T; Su MY
    Acad Radiol; 2019 Nov; 26(11):1526-1535. PubMed ID: 30713130
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.
    Hsu LM; Wang S; Walton L; Wang TW; Lee SH; Shih YI
    Front Neurosci; 2021; 15():801008. PubMed ID: 34975392
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.
    Jiang J; Hu YC; Tyagi N; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
    Med Phys; 2019 Oct; 46(10):4392-4404. PubMed ID: 31274206
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks.
    Xue YP; Jang H; Byra M; Cai ZY; Wu M; Chang EY; Ma YJ; Du J
    Eur Radiol; 2021 Oct; 31(10):7653-7663. PubMed ID: 33783571
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automated 3D U-net based segmentation of neonatal cerebral ventricles from 3D ultrasound images.
    Szentimrey Z; de Ribaupierre S; Fenster A; Ukwatta E
    Med Phys; 2022 Feb; 49(2):1034-1046. PubMed ID: 34958147
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.
    Chen T; Su ZH; Liu Z; Wang M; Cui ZF; Zhao L; Yang LJ; Zhang WC; Liu X; Liu J; Tan SY; Li SL; Feng QJ; Pang SM; Lu H
    Orthop Surg; 2022 Sep; 14(9):2256-2264. PubMed ID: 35979964
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears?
    Kim H; Shin K; Kim H; Lee ES; Chung SW; Koh KH; Kim N
    PLoS One; 2022; 17(10):e0274075. PubMed ID: 36215291
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.