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Items: 39

1.

Cerebral blood volume and apparent diffusion coefficient - Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma.

Petrova L, Korfiatis P, Petr O, LaChance DH, Parney I, Buckner JC, Erickson BJ.

J Neurol Sci. 2019 Aug 23;405:116433. doi: 10.1016/j.jns.2019.116433. [Epub ahead of print]

2.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Philbrick KA, Weston AD, Akkus Z, Kline TL, Korfiatis P, Sakinis T, Kostandy P, Boonrod A, Zeinoddini A, Takahashi N, Erickson BJ.

J Digit Imaging. 2019 Aug;32(4):571-581. doi: 10.1007/s10278-019-00232-0.

3.

Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO).

Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC.

Tomography. 2019 Mar;5(1):110-117. doi: 10.18383/j.tom.2018.00041.

4.

Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas.

Korfiatis P, Erickson B.

Clin Radiol. 2019 May;74(5):367-373. doi: 10.1016/j.crad.2019.01.028. Epub 2019 Mar 5. Review.

PMID:
30850092
5.

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.

Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, Sugimoto M, Takahashi N, Erickson BJ.

Radiology. 2019 Mar;290(3):669-679. doi: 10.1148/radiol.2018181432. Epub 2018 Dec 11.

PMID:
30526356
6.

What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images.

Philbrick KA, Yoshida K, Inoue D, Akkus Z, Kline TL, Weston AD, Korfiatis P, Takahashi N, Erickson BJ.

AJR Am J Roentgenol. 2018 Dec;211(6):1184-1193. doi: 10.2214/AJR.18.20331. Epub 2018 Nov 7.

PMID:
30403527
7.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project.

Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC.

AJNR Am J Neuroradiol. 2018 Jun;39(6):1008-1016. doi: 10.3174/ajnr.A5675. Epub 2018 May 24.

8.

Deep Learning in Radiology: Does One Size Fit All?

Erickson BJ, Korfiatis P, Kline TL, Akkus Z, Philbrick K, Weston AD.

J Am Coll Radiol. 2018 Mar;15(3 Pt B):521-526. doi: 10.1016/j.jacr.2017.12.027. Epub 2018 Jan 31.

9.
10.

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ.

J Digit Imaging. 2018 Apr;31(2):252-261. doi: 10.1007/s10278-017-0020-4.

11.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ.

J Digit Imaging. 2017 Oct;30(5):622-628. doi: 10.1007/s10278-017-0009-z.

12.

Quantitative MRI of kidneys in renal disease.

Kline TL, Edwards ME, Garg I, Irazabal MV, Korfiatis P, Harris PC, King BF, Torres VE, Venkatesh SK, Erickson BJ.

Abdom Radiol (NY). 2018 Mar;43(3):629-638. doi: 10.1007/s00261-017-1236-y.

13.

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, King BF, Torres VE, Erickson BJ.

J Digit Imaging. 2017 Aug;30(4):442-448. doi: 10.1007/s10278-017-9978-1.

14.

Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease.

Kline TL, Korfiatis P, Edwards ME, Bae KT, Yu A, Chapman AB, Mrug M, Grantham JJ, Landsittel D, Bennett WM, King BF, Harris PC, Torres VE, Erickson BJ; CRISP Investigators.

Kidney Int. 2017 Nov;92(5):1206-1216. doi: 10.1016/j.kint.2017.03.026. Epub 2017 May 20.

15.

Toolkits and Libraries for Deep Learning.

Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K.

J Digit Imaging. 2017 Aug;30(4):400-405. doi: 10.1007/s10278-017-9965-6. Review.

16.

Machine Learning for Medical Imaging.

Erickson BJ, Korfiatis P, Akkus Z, Kline TL.

Radiographics. 2017 Mar-Apr;37(2):505-515. doi: 10.1148/rg.2017160130. Epub 2017 Feb 17. Review.

17.

Acute Effect of Countermovement Jumping on Throwing Performance in Track and Field Athletes During Competition.

Karampatsos GP, Korfiatis PG, Zaras ND, Georgiadis GV, Terzis GD.

J Strength Cond Res. 2017 Feb;31(2):359-364. doi: 10.1519/JSC.0000000000001508.

PMID:
28125544
18.

A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography.

Peter R, Korfiatis P, Blezek D, Oscar Beitia A, Stepan-Buksakowska I, Horinek D, Flemming KD, Erickson BJ.

Med Phys. 2017 Jan;44(1):192-199. doi: 10.1002/mp.12015. Epub 2017 Jan 8.

19.

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation.

Korfiatis P, Kline TL, Kelm ZS, Carter RE, Hu LS, Erickson BJ.

Tomography. 2016 Dec;2(4):448-456. doi: 10.18383/j.tom.2016.00172.

20.

Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.

Korfiatis P, Kline TL, Erickson BJ.

Tomography. 2016 Dec;2(4):334-340. doi: 10.18383/j.tom.2016.00166.

21.

Semiautomated Segmentation of Polycystic Kidneys in T2-Weighted MR Images.

Kline TL, Edwards ME, Korfiatis P, Akkus Z, Torres VE, Erickson BJ.

AJR Am J Roentgenol. 2016 Sep;207(3):605-13. doi: 10.2214/AJR.15.15875. Epub 2016 Jun 24.

22.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.

Korfiatis P, Kline TL, Coufalova L, Lachance DH, Parney IF, Carter RE, Buckner JC, Erickson BJ.

Med Phys. 2016 Jun;43(6):2835-2844. doi: 10.1118/1.4948668.

23.

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma.

Hu LS, Kelm Z, Korfiatis P, Dueck AC, Elrod C, Ellingson BM, Kaufmann TJ, Eschbacher JM, Karis JP, Smith K, Nakaji P, Brinkman D, Pafundi D, Baxter LC, Erickson BJ.

AJNR Am J Neuroradiol. 2015 Dec;36(12):2242-9. doi: 10.3174/ajnr.A4451. Epub 2015 Sep 10.

24.

Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression.

Kline TL, Korfiatis P, Edwards ME, Warner JD, Irazabal MV, King BF, Torres VE, Erickson BJ.

Nephrol Dial Transplant. 2016 Feb;31(2):241-8. doi: 10.1093/ndt/gfv314. Epub 2015 Aug 31.

25.

MIRMAID: A Content Management System for Medical Image Analysis Research.

Korfiatis PD, Kline TL, Blezek DJ, Langer SG, Ryan WJ, Erickson BJ.

Radiographics. 2015 Sep-Oct;35(5):1461-8. doi: 10.1148/rg.2015140031. Epub 2015 Aug 18.

26.

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.

Akkus Z, Sedlar J, Coufalova L, Korfiatis P, Kline TL, Warner JD, Agrawal J, Erickson BJ.

Cancer Imaging. 2015 Aug 14;15:12. doi: 10.1186/s40644-015-0047-z.

27.

Selecting registration schemes in case of interstitial lung disease follow-up in CT.

Vlachopoulos G, Korfiatis P, Skiadopoulos S, Kazantzi A, Kalogeropoulou C, Pratikakis I, Costaridou L.

Med Phys. 2015 Aug;42(8):4511-25. doi: 10.1118/1.4923170.

PMID:
26233180
28.

Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression.

Kelm ZS, Korfiatis PD, Lingineni RK, Daniels JR, Buckner JC, Lachance DH, Parney IF, Carter RE, Erickson BJ.

J Med Imaging (Bellingham). 2015 Apr;2(2):026001. doi: 10.1117/1.JMI.2.2.026001. Epub 2015 May 26.

29.

Utilizing magnetization transfer imaging to investigate tissue remodeling in a murine model of autosomal dominant polycystic kidney disease.

Kline TL, Irazabal MV, Ebrahimi B, Hopp K, Udoji KN, Warner JD, Korfiatis P, Mishra PK, Macura SI, Venkatesh SK, Lerman LO, Harris PC, Torres VE, King BF, Erickson BJ.

Magn Reson Med. 2016 Apr;75(4):1466-73. doi: 10.1002/mrm.25701. Epub 2015 May 13.

30.

The impact of Platelet Rich Plasma (PRP) in osseointegration of oral implants in dental panoramic radiography: texture based evaluation.

Georgakopoulos I, Tsantis S, Georgakopoulos P, Korfiatis P, Fanti E, Martelli M, Costaridou L, Petsas T, Panayiotakis G, Martelli FS.

Clin Cases Miner Bone Metab. 2014 Jan;11(1):59-66.

31.

The basics of diffusion and perfusion imaging in brain tumors.

Korfiatis P, Erickson B.

Appl Radiol. 2014 Jul;43(7):22-29. Epub 2014 Jul 4. No abstract available.

32.

Automated 3D ιnterstitial lung disease εxtent quantification: performance evaluation and correlation to PFTs.

Kazantzi A, Costaridou L, Skiadopoulos S, Korfiatis P, Karahaliou A, Daoussis D, Andonopoulos A, Kalogeropoulou C.

J Digit Imaging. 2014 Jun;27(3):380-91. doi: 10.1007/s10278-013-9670-z.

33.

Improvement of hepatic encephalopathy by application of peritoneal dialysis in a patient with non-end-stage renal disease.

Pipili C, Polydorou A, Pantelias K, Korfiatis P, Nikolakopoulos F, Grapsa E.

Perit Dial Int. 2013 Mar-Apr;33(2):213-6. doi: 10.3747/pdi.2011.00271. No abstract available.

34.

Vessel tree segmentation in presence of interstitial lung disease in MDCT.

Korfiatis PD, Kalogeropoulou C, Karahaliou AN, Kazantzi AD, Costaridou LI.

IEEE Trans Inf Technol Biomed. 2011 Mar;15(2):214-20. doi: 10.1109/TITB.2011.2112668. Epub 2011 Feb 10.

PMID:
21317088
35.

Size-adapted microcalcification segmentation in mammography utilizing scale-space signatures.

Arikidis NS, Karahaliou A, Skiadopoulos S, Korfiatis P, Likaki E, Panayiotakis G, Costaridou L.

Comput Med Imaging Graph. 2010 Sep;34(6):487-93. doi: 10.1016/j.compmedimag.2009.12.009. Epub 2010 Jan 18.

PMID:
20080386
36.

Is there a role for B-cell depletion as therapy for scleroderma? A case report and review of the literature.

Daoussis D, Liossis SN, Tsamandas AC, Kalogeropoulou C, Kazantzi A, Korfiatis P, Yiannopoulos G, Andonopoulos AP.

Semin Arthritis Rheum. 2010 Oct;40(2):127-36. doi: 10.1016/j.semarthrit.2009.09.003. Epub 2009 Dec 11. Review.

PMID:
20004954
37.

Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT.

Korfiatis PD, Karahaliou AN, Kazantzi AD, Kalogeropoulou C, Costaridou LI.

IEEE Trans Inf Technol Biomed. 2010 May;14(3):675-80. doi: 10.1109/TITB.2009.2036166. Epub 2009 Nov 10.

PMID:
19906596
38.

Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT.

Korfiatis P, Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L.

Med Phys. 2008 Dec;35(12):5290-302.

PMID:
19175088
39.

Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT.

Korfiatis P, Skiadopoulos S, Sakellaropoulos P, Kalogeropoulou C, Costaridou L.

Br J Radiol. 2007 Dec;80(960):996-1004.

PMID:
18065645

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