BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

370 related articles for article (PubMed ID: 30390179)

  • 1. Dual-modality multi-atlas segmentation of torso organs from [
    Wang H; Zhang N; Huo L; Zhang B
    Int J Comput Assist Radiol Surg; 2019 Mar; 14(3):473-482. PubMed ID: 30390179
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices.
    Takeda K; Hara T; Zhou X; Katafuchi T; Kato M; Ito S; Ishihara K; Kumita S; Fujita H
    Int J Comput Assist Radiol Surg; 2017 May; 12(5):777-787. PubMed ID: 28168681
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Quantification of body-torso-wide tissue composition on low-dose CT images via automatic anatomy recognition.
    Liu T; Udupa JK; Miao Q; Tong Y; Torigian DA
    Med Phys; 2019 Mar; 46(3):1272-1285. PubMed ID: 30614020
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Fully Automated, Semantic Segmentation of Whole-Body
    Shiyam Sundar LK; Yu J; Muzik O; Kulterer OC; Fueger B; Kifjak D; Nakuz T; Shin HM; Sima AK; Kitzmantl D; Badawi RD; Nardo L; Cherry SR; Spencer BA; Hacker M; Beyer T
    J Nucl Med; 2022 Dec; 63(12):1941-1948. PubMed ID: 35772962
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatic localization of solid organs on 3D CT images by a collaborative majority voting decision based on ensemble learning.
    Zhou X; Wang S; Chen H; Hara T; Yokoyama R; Kanematsu M; Fujita H
    Comput Med Imaging Graph; 2012 Jun; 36(4):304-13. PubMed ID: 22421130
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.
    Arabi H; Koutsouvelis N; Rouzaud M; Miralbell R; Zaidi H
    Phys Med Biol; 2016 Sep; 61(17):6531-52. PubMed ID: 27524504
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network.
    Wang H; Han Y; Chen Z; Hu R; Chatziioannou AF; Zhang B
    Phys Med Biol; 2019 Dec; 64(24):245014. PubMed ID: 31747654
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.
    Arabi H; Zaidi H
    Magn Reson Med; 2022 Feb; 87(2):686-701. PubMed ID: 34480771
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [
    Krokos G; Kotwal T; Malaih A; Barrington S; Jackson P; Hicks RJ; Marsden PK; Fischer BM
    Biomed Phys Eng Express; 2024 Jan; 10(2):. PubMed ID: 38100790
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.
    Li D; Liu L; Chen J; Li H; Yin Y; Ibragimov B; Xing L
    Phys Med Biol; 2017 Jan; 62(1):272-288. PubMed ID: 27991439
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI.
    Mehranian A; Arabi H; Zaidi H
    Neuroimage; 2016 Apr; 130():123-133. PubMed ID: 26853602
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation.
    Bi L; Fulham M; Li N; Liu Q; Song S; Dagan Feng D; Kim J
    Comput Methods Programs Biomed; 2021 May; 203():106043. PubMed ID: 33744750
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.
    Shiri I; Amini M; Yousefirizi F; Vafaei Sadr A; Hajianfar G; Salimi Y; Mansouri Z; Jenabi E; Maghsudi M; Mainta I; Becker M; Rahmim A; Zaidi H
    Med Phys; 2024 Jan; 51(1):319-333. PubMed ID: 37475591
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.
    Zhuang X; Bai W; Song J; Zhan S; Qian X; Shi W; Lian Y; Rueckert D
    Med Phys; 2015 Jul; 42(7):3822-33. PubMed ID: 26133584
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Automatic anatomy recognition in whole-body PET/CT images.
    Wang H; Udupa JK; Odhner D; Tong Y; Zhao L; Torigian DA
    Med Phys; 2016 Jan; 43(1):613. PubMed ID: 26745953
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PSMA-PET improves deep learning-based automated CT kidney segmentation.
    Leube J; Horn M; Hartrampf PE; Buck AK; Lassmann M; Tran-Gia J
    Z Med Phys; 2024 May; 34(2):231-241. PubMed ID: 37666698
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PSMA-Hornet: Fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images.
    Klyuzhin IS; Chaussé G; Bloise I; Harsini S; Ferres JL; Uribe C; Rahmim A
    Med Phys; 2024 Feb; 51(2):1203-1216. PubMed ID: 37544015
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
    Zhou X
    Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.
    Lee YS; Hong N; Witanto JN; Choi YR; Park J; Decazes P; Eude F; Kim CO; Chang Kim H; Goo JM; Rhee Y; Yoon SH
    Clin Nutr; 2021 Aug; 40(8):5038-5046. PubMed ID: 34365038
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.