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.


Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1189 related articles for article (PubMed ID: 29863600)

  • 1. Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
    Perkuhn M; Stavrinou P; Thiele F; Shakirin G; Mohan M; Garmpis D; Kabbasch C; Borggrefe J
    Invest Radiol; 2018 Nov; 53(11):647-654. PubMed ID: 29863600
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.
    Wan Y; Rahmat R; Price SJ
    Acta Neurochir (Wien); 2020 Dec; 162(12):3067-3080. PubMed ID: 32662042
    [TBL] [Abstract][Full Text] [Related]  

  • 5. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
    Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
    Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.
    Conte GM; Weston AD; Vogelsang DC; Philbrick KA; Cai JC; Barbera M; Sanvito F; Lachance DH; Jenkins RB; Tobin WO; Eckel-Passow JE; Erickson BJ
    Radiology; 2021 May; 299(2):313-323. PubMed ID: 33687284
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
    Naceur MB; Saouli R; Akil M; Kachouri R
    Comput Methods Programs Biomed; 2018 Nov; 166():39-49. PubMed ID: 30415717
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.
    Jünger ST; Hoyer UCI; Schaufler D; Laukamp KR; Goertz L; Thiele F; Grunz JP; Schlamann M; Perkuhn M; Kabbasch C; Persigehl T; Grau S; Borggrefe J; Scheffler M; Shahzad R; Pennig L
    J Magn Reson Imaging; 2021 Nov; 54(5):1608-1622. PubMed ID: 34032344
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded.
    Porz N; Habegger S; Meier R; Verma R; Jilch A; Fichtner J; Knecht U; Radina C; Schucht P; Beck J; Raabe A; Slotboom J; Reyes M; Wiest R
    PLoS One; 2016; 11(11):e0165302. PubMed ID: 27806121
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.
    Lavdas I; Glocker B; Kamnitsas K; Rueckert D; Mair H; Sandhu A; Taylor SA; Aboagye EO; Rockall AG
    Med Phys; 2017 Oct; 44(10):5210-5220. PubMed ID: 28756622
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.
    Di Ieva A; Russo C; Liu S; Jian A; Bai MY; Qian Y; Magnussen JS
    Neuroradiology; 2021 Aug; 63(8):1253-1262. PubMed ID: 33501512
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study.
    Chen H; Li S; Zhang Y; Liu L; Lv X; Yi Y; Ruan G; Ke C; Feng Y
    Eur Radiol; 2022 Oct; 32(10):7248-7259. PubMed ID: 35420299
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights.
    Khodadadi Shoushtari F; Sina S; Dehkordi ANV
    Phys Med; 2022 Aug; 100():51-63. PubMed ID: 35732092
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.
    Ribalta Lorenzo P; Nalepa J; Bobek-Billewicz B; Wawrzyniak P; Mrukwa G; Kawulok M; Ulrych P; Hayball MP
    Comput Methods Programs Biomed; 2019 Jul; 176():135-148. PubMed ID: 31200901
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.
    Grimm F; Edl F; Kerscher SR; Nieselt K; Gugel I; Schuhmann MU
    Acta Neurochir (Wien); 2020 Oct; 162(10):2463-2474. PubMed ID: 32583085
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.
    Soerensen SJC; Fan RE; Seetharaman A; Chen L; Shao W; Bhattacharya I; Kim YH; Sood R; Borre M; Chung BI; To'o KJ; Rusu M; Sonn GA
    J Urol; 2021 Sep; 206(3):604-612. PubMed ID: 33878887
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.
    Pennig L; Shahzad R; Caldeira L; Lennartz S; Thiele F; Goertz L; Zopfs D; Meißner AK; Fürtjes G; Perkuhn M; Kabbasch C; Grau S; Borggrefe J; Laukamp KR
    AJNR Am J Neuroradiol; 2021 Apr; 42(4):655-662. PubMed ID: 33541907
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 60.