BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

254 related articles for article (PubMed ID: 36711966)

  • 1. Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.
    Kazerooni AF; Arif S; Madhogarhia R; Khalili N; Haldar D; Bagheri S; Familiar AM; Anderson H; Haldar S; Tu W; Kim MC; Viswanathan K; Muller S; Prados M; Kline C; Vidal L; Aboian M; Storm PB; Resnick AC; Ware JB; Vossough A; Davatzikos C; Nabavizadeh A
    medRxiv; 2023 Jan; ():. PubMed ID: 36711966
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.
    Fathi Kazerooni A; Arif S; Madhogarhia R; Khalili N; Haldar D; Bagheri S; Familiar AM; Anderson H; Haldar S; Tu W; Chul Kim M; Viswanathan K; Muller S; Prados M; Kline C; Vidal L; Aboian M; Storm PB; Resnick AC; Ware JB; Vossough A; Davatzikos C; Nabavizadeh A
    Neurooncol Adv; 2023; 5(1):vdad027. PubMed ID: 37051331
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.
    Pennig L; Hoyer UCI; Goertz L; Shahzad R; Persigehl T; Thiele F; Perkuhn M; Ruge MI; Kabbasch C; Borggrefe J; Caldeira L; Laukamp KR
    J Magn Reson Imaging; 2021 Jan; 53(1):259-268. PubMed ID: 32662130
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.
    Laukamp KR; Pennig L; Thiele F; Reimer R; Görtz L; Shakirin G; Zopfs D; Timmer M; Perkuhn M; Borggrefe J
    Clin Neuroradiol; 2021 Jun; 31(2):357-366. PubMed ID: 32060575
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net.
    Lin M; Momin S; Lei Y; Wang H; Curran WJ; Liu T; Yang X
    Med Phys; 2021 Aug; 48(8):4365-4374. PubMed ID: 34101845
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.
    Gross M; Huber S; Arora S; Ze'evi T; Haider SP; Kucukkaya AS; Iseke S; Kuhn TN; Gebauer B; Michallek F; Dewey M; Vilgrain V; Sartoris R; Ronot M; Jaffe A; Strazzabosco M; Chapiro J; Onofrey JA
    Eur Radiol; 2024 Jan; ():. PubMed ID: 38217704
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.
    Kickingereder P; Isensee F; Tursunova I; Petersen J; Neuberger U; Bonekamp D; Brugnara G; Schell M; Kessler T; Foltyn M; Harting I; Sahm F; Prager M; Nowosielski M; Wick A; Nolden M; Radbruch A; Debus J; Schlemmer HP; Heiland S; Platten M; von Deimling A; van den Bent MJ; Gorlia T; Wick W; Bendszus M; Maier-Hein KH
    Lancet Oncol; 2019 May; 20(5):728-740. PubMed ID: 30952559
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.
    Ye Z; Saraf A; Ravipati Y; Hoebers F; Zha Y; Zapaishchykova A; Likitlersuang J; Tishler RB; Schoenfeld JD; Margalit DN; Haddad RI; Mak RH; Naser M; Wahid KA; Sahlsten J; Jaskari J; Kaski K; Mäkitie AA; Fuller CD; Aerts HJWL; Kann BH
    medRxiv; 2023 Mar; ():. PubMed ID: 36945519
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation.
    Dieckhaus H; Meijboom R; Okar S; Wu T; Parvathaneni P; Mina Y; Chandran S; Waldman AD; Reich DS; Nair G
    Top Magn Reson Imaging; 2022 Jun; 31(3):31-39. PubMed ID: 35767314
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI.
    Momin S; Lei Y; Tian Z; Roper J; Lin J; Kahn S; Shu HK; Bradley J; Liu T; Yang X
    Phys Med Biol; 2022 Apr; 67(8):. PubMed ID: 35299156
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.
    Zhao Y; Tang C; Cui B; Somasundaram A; Raspe J; Hu X; Holzapfel C; Junker D; Hauner H; Menze B; Wu M; Karampinos D
    Quant Imaging Med Surg; 2023 Jul; 13(7):4699-4715. PubMed ID: 37456284
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.
    Zhang L; Mohamed AA; Chai R; Guo Y; Zheng B; Wu S
    J Magn Reson Imaging; 2020 Feb; 51(2):635-643. PubMed ID: 31301201
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.
    Rudie JD; Weiss DA; Saluja R; Rauschecker AM; Wang J; Sugrue L; Bakas S; Colby JB
    Front Comput Neurosci; 2019; 13():84. PubMed ID: 31920609
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors.
    Raut P; Baldini G; Schöneck M; Caldeira L
    Front Radiol; 2023; 3():1336902. PubMed ID: 38304344
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.
    Brugnara G; Isensee F; Neuberger U; Bonekamp D; Petersen J; Diem R; Wildemann B; Heiland S; Wick W; Bendszus M; Maier-Hein K; Kickingereder P
    Eur Radiol; 2020 Apr; 30(4):2356-2364. PubMed ID: 31900702
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.
    Laukamp KR; Thiele F; Shakirin G; Zopfs D; Faymonville A; Timmer M; Maintz D; Perkuhn M; Borggrefe J
    Eur Radiol; 2019 Jan; 29(1):124-132. PubMed ID: 29943184
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.
    Aboian M; Bousabarah K; Kazarian E; Zeevi T; Holler W; Merkaj S; Cassinelli Petersen G; Bahar R; Subramanian H; Sunku P; Schrickel E; Bhawnani J; Zawalich M; Mahajan A; Malhotra A; Payabvash S; Tocino I; Lin M; Westerhoff M
    Front Neurosci; 2022; 16():860208. PubMed ID: 36312024
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability.
    Opfer R; Krüger J; Spies L; Ostwaldt AC; Kitzler HH; Schippling S; Buchert R
    Eur Radiol; 2023 Mar; 33(3):1852-1861. PubMed ID: 36264314
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.