Publications

  1. Nanaa A, Akkus Z, Lee WY, Pantanowitz L, Salama ME. Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders. Pathology. 2021 Apr; 53 (3):400-407 Epub 2021 Feb 25
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  2. Akkus Z, Aly YH, Attia IZ, Lopez-Jimenez F, Arruda-Olson AM, Pellikka PA, Pislaru SV, Kane GC, Friedman PA, Oh JK. Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review. J Clin Med. 2021 Mar 30; 10 (7)
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  3. Akkus Z, Kostandy P, Philbrick KA, Erickson BJ. Robust brain extraction tool for ct head images Neurocomputing. 2020 Jun 7; 392:189-95
  4. Kwon JM, Kim KH, Akkus Z, Jeon KH, Park J, Oh BH. Artificial intelligence for detecting mitral regurgitation using electrocardiography. J Electrocardiol. 2020 Mar - Apr; 59:151-157 Epub 2020 Feb 27
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  5. Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, Erickson BJ. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol. 2019 Sep; 16 (9 Pt B):1318-1328
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  6. Philbrick KA, Weston AD, Akkus Z, Kline TL, Korfiatis P, Sakinis T, Kostandy P, Boonrod A, Zeinoddini A, Takahashi N, Erickson BJ. RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. J Digit Imaging. 2019 Aug; 32 (4):571-581
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  7. Akkus Z, Boonrod A, Siddiquee MR, Philbrick KA, Stan MN, Castro RM, Erickson D, Callstrom MR, Erickson BJ. Reduction of unnecessary thyroid biopsies using deep learning Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019; 10949:109490W
  8. Philbrick KA, Yoshida K, Inoue D, Akkus Z, Kline TL, Weston AD, Korfiatis P, Takahashi N, Erickson BJ. What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images. AJR Am J Roentgenol. 2018 Dec; 211 (6):1184-1193 Epub 2018 Nov 07
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  9. Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ. Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging. 2018 Apr; 31 (2):252-261
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  10. Erickson BJ, Korfiatis P, Kline TL, Akkus Z, Philbrick K, Weston AD. Deep Learning in Radiology: Does One Size Fit All? J Am Coll Radiol. 2018 Mar; 15 (3 Pt B):521-526 Epub 2018 Jan 31
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  11. Akkus Z, Kostandy PM, Philbrick KA, Erickson BJ. Extraction of brain tissue from CT head images using fully convolutional neural networks Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018; 10574:1057420
  12. Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K. Toolkits and Libraries for Deep Learning. J Digit Imaging. 2017 Aug; 30 (4):400-405
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  13. Akkus Z, Ali I, Sedlar J, Agrawal JP, Parney IF, Giannini C, Erickson BJ. Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. J Digit Imaging. 2017 Aug; 30 (4):469-476
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  14. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017 Aug; 30 (4):449-459
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  15. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017 Mar-Apr; 37 (2):505-515 Epub 2017 Feb 17
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  16. Akkus Z, Bayat M, Cheong M, Viksit K, Erickson BJ, Alizad A, Fatemi M. Fully Automated and Robust Tracking of Transient Waves in Structured Anatomies Using Dynamic Programming. Ultrasound Med Biol. 2016 Oct; 42 (10):2504-12 Epub 2016 July 15
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  17. Kline TL, Edwards ME, Korfiatis P, Akkus Z, Torres VE, Erickson BJ. Semiautomated Segmentation of Polycystic Kidneys in T2-Weighted MR Images. AJR Am J Roentgenol. 2016 Sep; 207 (3):605-13 Epub 2016 June 24
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  18. Akkus A, Ali I, Sedlar J, Kline TL, Parney IF, Giannini C, Erickson BJ. Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning IEEE Transactions on Image Processing (submitted). 2016.
  19. Akkus Z, Sedlar J, Coufalova L, Korfiatis P, Kline TL, Warner JD, Agrawal J, Erickson BJ. Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging. 2015 Aug 14; 15:12
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  20. Daeichin V., Akkus Z, Skachkov I et al.. Quantification of bound microbubbles in ultrasound molecular imaging IEEE Trans Ultrason Ferroelectr Freq Control.2015;62:(6)1190-200.
  21. Akkus Z, van Burken G, van den Oord S. C.H. et al. Carotid Intraplaque Neovascularization Software (CINQS) IEEE Journal of Biomedical and Health Informatics.2015;19:(1)332-338.
  22. Akkus Z, Carvalho D.D.B, Bosch J.G. et al. Lumen Segmentation and Motion Estimation in B-mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients with Atherosclerotic Plaque IEEE Transactions on Medical Imaging.2014;34:(4)983-93.
  23. Akkus Z, Carvalho D.D.B., Klein S. et al.. Fully Automated Carotid Plaque Segmentation in Combined Contrast-Enhanced and B-Mode Ultrasound. Ultrasound Med & Biology.2014;41:(2)517-531.
  24. Van den Oord S.C.H., Akkus Z, Renaud G. et al. Assessment of carotid atherosclerosis, intraplaque neovascularization, and plaque ulceration using quantitative contrast-enhanced ultrasound in asymptomatic patients with diabetes mellitus. Eur Heart J Cardiovasc Imaging.2014;15:(11)1213-8.
  25. Van den Oord S. C.H., Akkus Z, Bosch J.G. et al. Quantitative Contrast-Enhanced Ultrasound of Intraplaque Neovascularization in Patients with Carotid Atherosclerosis Ultraschall Med..2014;36:(2)154-61.
  26. Carvalho D.D.B., Klein S., Akkus Z et al.. Automated Joint Intensity-and-Point Based Registration of Free-hand B-Mode Ultrasound and MRI of the Carotid Artery Medical Physics.2014;41:(5)052904.
  27. Van den Oord S. C.H., van der Burg J., Akkus Z et al. Impact of Gender on the Density of Intraplaque Neovascularization: A Quantitative Contrast-Enhanced Ultrasound Study Atherosclerosis.2014;233:(2)461-466.
  28. Akkus Z, Hoogi A., Renaud G. et al.. New Quantification Methods for Carotid Intraplaque Neovascularization using Contrast Enhanced Ultrasound. Ultrasound Medicine and Biology.2013;40:(1)25-36.
  29. Van den Oord S.C.H., Akkus Z, Renaud G. et al.. Assessment of Subclinical Atherosclerosis and Intraplaque Neovascularization using Quantitative Contrast-Enhanced Ultrasound in Patients with Familial Hypercholesterolemia Atherosclerosis Atherosclerosis.2013;231:(1)107-113.
  30. Hoogi A., Akkus Z, van den Oord S. et al.. Quantitative analysis of ultrasound contrast flow behavior in Carotid Plaque Neovasculature Ultrasound Med. Biol..2012;38:(12)2072-2083.
  31. Van den Oord S. C.H., Ten Kate G. L., Akkus Z et al.. Assessment of subclinical atherosclerosis using contrast-enhanced ultrasound Eur Heart J Cardiovasc Imaging.2012;14:(1)56-61.
  32. Carvalho D.D.B., Klein S., Akkus Z. Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound Int J Comput Assist Radiol Surg..2011;7:(2)207-15.
  33. Ten Kate G.L., Renaud G, Akkus Z et al.. Far Wall Pseudoenhancement During Contrast Enhanced Ultrasound of the Carotid Arteries: Clinical Description and in Vitro Reproduction. Ultrasound Med Biol..2011;38:(4)593-600.
  34. Akkus Z, Ramnarine K. V.. Dynamic Assesment of Carotid Plaque Motion Ultrasound.2010;18:140-147.