Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 35

1.

Patch-Based Generative Adversarial Neural Network Models for Head and Neck MR-Only Planning.

Klages P, Benslimane I, Riyahi S, Jiang J, Hunt M, Deasy JO, Veeraraghavan H, Tyagi N.

Med Phys. 2019 Nov 16. doi: 10.1002/mp.13927. [Epub ahead of print]

PMID:
31733164
2.

MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.

Bhatia A, Birger M, Veeraraghavan H, Um H, Tixier F, Mckenney AS, Cugliari M, Caviasco A, Bialczak A, Malani R, Flynn J, Zhang Z, Yang TJ, Santomasso BD, Shoushtari AN, Young RJ.

Neuro Oncol. 2019 Oct 17. pii: noz141. doi: 10.1093/neuonc/noz141. [Epub ahead of print]

PMID:
31621883
3.

A rectal cancer organoid platform to study individual responses to chemoradiation.

Ganesh K, Wu C, O'Rourke KP, Szeglin BC, Zheng Y, Sauvé CG, Adileh M, Wasserman I, Marco MR, Kim AS, Shady M, Sanchez-Vega F, Karthaus WR, Won HH, Choi SH, Pelossof R, Barlas A, Ntiamoah P, Pappou E, Elghouayel A, Strong JS, Chen CT, Harris JW, Weiser MR, Nash GM, Guillem JG, Wei IH, Kolesnick RN, Veeraraghavan H, Ortiz EJ, Petkovska I, Cercek A, Manova-Todorova KO, Saltz LB, Lavery JA, DeMatteo RP, Massagué J, Paty PB, Yaeger R, Chen X, Patil S, Clevers H, Berger MF, Lowe SW, Shia J, Romesser PB, Dow LE, Garcia-Aguilar J, Sawyers CL, Smith JJ.

Nat Med. 2019 Oct;25(10):1607-1614. doi: 10.1038/s41591-019-0584-2. Epub 2019 Oct 7.

PMID:
31591597
4.

Dynamic multiatlas selection-based consensus segmentation of head and neck structures from CT images.

Haq R, Berry SL, Deasy JO, Hunt M, Veeraraghavan H.

Med Phys. 2019 Dec;46(12):5612-5622. doi: 10.1002/mp.13854. Epub 2019 Oct 31.

PMID:
31587300
5.

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. doi: 10.1002/mp.13695. Epub 2019 Aug 20.

PMID:
31274206
6.

Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets.

Um H, Tixier F, Bermudez D, Deasy JO, Young RJ, Veeraraghavan H.

Phys Med Biol. 2019 Aug 21;64(16):165011. doi: 10.1088/1361-6560/ab2f44.

PMID:
31272093
7.

Automated Breast Density Measurements From Chest Computed Tomography Scans.

Qureshi TA, Veeraraghavan H, Sung JS, Kaplan JB, Flynn J, Tonorezos ES, Wolden SL, Morris EA, Oeffinger KC, Pike MC, Moskowitz CS.

J Med Syst. 2019 Jun 22;43(8):242. doi: 10.1007/s10916-019-1363-9.

PMID:
31230138
8.

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.

Tixier F, Um H, Young RJ, Veeraraghavan H.

Med Phys. 2019 Aug;46(8):3582-3591. doi: 10.1002/mp.13624. Epub 2019 Jul 5.

PMID:
31131906
9.

Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations.

Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, Marco M, Cheng CT, Gonen M, Golia Pernicka JS, Gollub MJ, Garcia-Aguillar J, Petkovska I.

Eur J Radiol. 2019 Apr;113:174-181. doi: 10.1016/j.ejrad.2019.02.022. Epub 2019 Feb 18.

PMID:
30927944
10.

Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone.

Tixier F, Um H, Bermudez D, Iyer A, Apte A, Graham MS, Nevel KS, Deasy JO, Young RJ, Veeraraghavan H.

Oncotarget. 2019 Jan 18;10(6):660-672. doi: 10.18632/oncotarget.26578. eCollection 2019 Jan 18.

11.

Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network.

Wang C, Tyagi N, Rimner A, Hu YC, Veeraraghavan H, Li G, Hunt M, Mageras G, Zhang P.

Radiother Oncol. 2019 Feb;131:101-107. doi: 10.1016/j.radonc.2018.10.037. Epub 2018 Dec 31.

12.

Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer.

Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, Sutton EJ, Hricak H, Sala E, Vargas HA.

Abdom Radiol (NY). 2019 Jun;44(6):2040-2047. doi: 10.1007/s00261-018-1840-5.

PMID:
30474722
13.

Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.

Jiang J, Hu YC, Tyagi N, Zhang P, Rimner A, Mageras GS, Deasy JO, Veeraraghavan H.

Med Image Comput Comput Assist Interv. 2018 Sep;11071:777-785. doi: 10.1007/978-3-030-00934-2_86.

14.

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

Yang J, Veeraraghavan H, Armato SG 3rd, Farahani K, Kirby JS, Kalpathy-Kramer J, van Elmpt W, Dekker A, Han X, Feng X, Aljabar P, Oliveira B, van der Heyden B, Zamdborg L, Lam D, Gooding M, Sharp GC.

Med Phys. 2018 Oct;45(10):4568-4581. doi: 10.1002/mp.13141. Epub 2018 Sep 19.

15.

Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images.

Jiang J, Hu YC, Liu CJ, Halpenny D, Hellmann MD, Deasy JO, Mageras G, Veeraraghavan H.

IEEE Trans Med Imaging. 2019 Jan;38(1):134-144. doi: 10.1109/TMI.2018.2857800. Epub 2018 Jul 23.

16.

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

Apte AP, Iyer A, Crispin-Ortuzar M, Pandya R, van Dijk LV, Spezi E, Thor M, Um H, Veeraraghavan H, Oh JH, Shukla-Dave A, Deasy JO.

Med Phys. 2018 Jun 13. doi: 10.1002/mp.13046. [Epub ahead of print]

PMID:
29896896
17.

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

Veeraraghavan H, Dashevsky BZ, Onishi N, Sadinski M, Morris E, Deasy JO, Sutton EJ.

Sci Rep. 2018 Mar 19;8(1):4838. doi: 10.1038/s41598-018-22980-9.

18.

MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.

Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, Sala E, Garcia-Aguilar J, Gollub MJ, Petkovska I.

Radiology. 2018 Jun;287(3):833-843. doi: 10.1148/radiol.2018172300. Epub 2018 Mar 7.

19.

Heterogeneous Tumor-Immune Microenvironments among Differentially Growing Metastases in an Ovarian Cancer Patient.

Jiménez-Sánchez A, Memon D, Pourpe S, Veeraraghavan H, Li Y, Vargas HA, Gill MB, Park KJ, Zivanovic O, Konner J, Ricca J, Zamarin D, Walther T, Aghajanian C, Wolchok JD, Sala E, Merghoub T, Snyder A, Miller ML.

Cell. 2017 Aug 24;170(5):927-938.e20. doi: 10.1016/j.cell.2017.07.025.

20.

Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy.

Farjam R, Tyagi N, Veeraraghavan H, Apte A, Zakian K, Hunt MA, Deasy JO.

Med Phys. 2017 Jul;44(7):3706-3717. doi: 10.1002/mp.12303. Epub 2017 Jun 1.

21.

A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome.

Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, Sosa R, Soslow RA, Levine DA, Weigelt B, Aghajanian C, Hricak H, Deasy J, Snyder A, Sala E.

Eur Radiol. 2017 Sep;27(9):3991-4001. doi: 10.1007/s00330-017-4779-y. Epub 2017 Mar 13.

22.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, Nougaret S, Sosa RE, Vargas HA, Soslow RA, Abu-Rustum NR, Hricak H, Sala E.

Eur Radiol. 2017 Jul;27(7):2903-2915. doi: 10.1007/s00330-016-4623-9. Epub 2016 Dec 5.

23.

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.

Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, Weigelt B, Vargas HA.

Clin Radiol. 2017 Jan;72(1):3-10. doi: 10.1016/j.crad.2016.09.013. Epub 2016 Oct 11. Review.

24.

A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis.

Saleh Z, Thor M, Apte AP, Sharp G, Tang X, Veeraraghavan H, Muren L, Deasy J.

Phys Med Biol. 2016 Aug 21;61(16):6172-80. doi: 10.1088/0031-9155/61/16/6172. Epub 2016 Jul 29.

25.

AUTOMATIC DETECTION AND TRACKING OF LONGITUDINAL CHANGES OF MULTIPLE BONE METASTASES FROM DUAL ENERGY CT.

Fehr D, Schmidtlein CR, Hwang S, Deasy JO, Veeraraghavan H.

Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:168-171. doi: 10.1109/ISBI.2016.7493236. Epub 2016 Jun 16.

26.

Breast cancer molecular subtype classifier that incorporates MRI features.

Sutton EJ, Dashevsky BZ, Oh JH, Veeraraghavan H, Apte AP, Thakur SB, Morris EA, Deasy JO.

J Magn Reson Imaging. 2016 Jul;44(1):122-9. doi: 10.1002/jmri.25119. Epub 2016 Jan 12.

27.

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, Sala E, Hricak H, Deasy JO.

Proc Natl Acad Sci U S A. 2015 Nov 17;112(46):E6265-73. doi: 10.1073/pnas.1505935112. Epub 2015 Nov 2.

28.

Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.

Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, Zheng J, Goldman D, Moskowitz C, Fine SW, Reuter VE, Eastham J, Sala E, Vargas HA.

Eur Radiol. 2015 Oct;25(10):2840-50. doi: 10.1007/s00330-015-3701-8. Epub 2015 May 21.

29.

Simultaneous segmentation and iterative registration method for computing ADC with reduced artifacts from DW-MRI.

Veeraraghavan H, Do RK, Reidy DL, Deasy JO.

Med Phys. 2015 May;42(5):2249-60. doi: 10.1118/1.4916799.

PMID:
25979019
30.

Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.

Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB, Deasy JO, Morris EA.

J Magn Reson Imaging. 2015 Nov;42(5):1398-406. doi: 10.1002/jmri.24890. Epub 2015 Apr 7.

31.

Vision 20/20: perspectives on automated image segmentation for radiotherapy.

Sharp G, Fritscher KD, Pekar V, Peroni M, Shusharina N, Veeraraghavan H, Yang J.

Med Phys. 2014 May;41(5):050902. doi: 10.1118/1.4871620. Review.

32.

The distance discordance metric-a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration.

Saleh ZH, Apte AP, Sharp GC, Shusharina NP, Wang Y, Veeraraghavan H, Thor M, Muren LP, Rao SS, Lee NY, Deasy JO.

Phys Med Biol. 2014 Feb 7;59(3):733-46. doi: 10.1088/0031-9155/59/3/733. Epub 2014 Jan 20.

33.

Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search.

Veeraraghavan H, Miller JV.

Neuroinformatics. 2014 Apr;12(2):245-59. doi: 10.1007/s12021-013-9202-5.

34.

GBM volumetry using the 3D Slicer medical image computing platform.

Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R.

Sci Rep. 2013;3:1364. doi: 10.1038/srep01364.

35.

ACTIVE LEARNING GUIDED INTERACTIONS FOR CONSISTENT IMAGE SEGMENTATION WITH REDUCED USER INTERACTIONS.

Veeraraghavan H, Miller JV.

Proc IEEE Int Symp Biomed Imaging. 2011 Mar-Apr;2011:1645-1648. doi: 10.1109/ISBI.2011.5872719. Epub 2011 Jun 9.

Supplemental Content

Loading ...
Support Center