Publications

1. Vahdati S, Khosravi B, Robinson KA, Rouzrokh P, Moassefi M, Akkus Z, Erickson BJ. A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging. Bioengineering (Basel). 2024 Jun 25; 11 (7) Epub 2024 June 25
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2. Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z. Artificial Intelligence Advances in Transplant Pathology. Bioengineering (Basel). 2023 Sep 04; 10(9).
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3. Rahman MA, Clinch M, Reynolds J, Dangott B, Meza Villegas DM, Nassar A, Hata DJ, Akkus Z. Classification of fungal genera from microscopic images using artificial intelligence. J Pathol Inform. 2023; 14:100314 Epub 2023 Apr 23
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4. Kuroiwa T, Jagtap J, Starlinger J, Lui H, Akkus Z, Erickson B, Amadio P. Deep Learning Estimation of Median Nerve Volume Using Ultrasound Imaging in a Human Cadaver Model. Ultrasound Med Biol. 2022 Nov; 48 (11):2237-2248 Epub 2022 Aug 10
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5. Jagtap JM, Gregory AV, Homes HL, Wright DE, Edwards ME, Akkus Z, Erickson BJ, Kline TL. Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements. Abdom Radiol (NY). 2022 Jul; 47 (7):2408-2419 Epub 2022 Apr 27
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6. Boonrod A, Akkus Z, Castro MR, Zeinodini A, Philbrick K, Stan M, Erickson D, Erickson B. Thyroid Nodule Size as a Predictor of Malignancy in Follicular and Hurthle Neoplasms. Asian Pac J Cancer Prev. 2021 Aug 1; 22 (8):2597-2602 Epub 2021 Aug 01
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7. Osman M, Akkus Z, Jevremovic D, Nguyen PL, Roh D, Al-Kali A, Patnaik MM, Nanaa A, Rizk S, Salama ME. Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology. J Clin Med. 2021 May 24; 10 (11)
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8. 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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9. 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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10. Cai JC, Akkus Z, Philbrick KA, Boonrod A, Hoodeshenas S, Weston AD, Rouzrokh P, Conte GM, Zeinoddini A, Vogelsang DC, Huang Q, Erickson BJ. Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning. Radiol Artif Intell. 2020 Sep; 2 (5):e190183 Epub 2020 Sept 30
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11. Akkus Z, Kostandy P, Philbrick KA, Erickson BJ. Robust brain extraction tool for CT head images. Neurocomputing. 2020 Jun 7; 392:189-95. Epub 1900 Jan 01.
    
12. 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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13. 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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14. 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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15. 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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16. 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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17. 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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18. 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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19. 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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20. 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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21. 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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22. 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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23. 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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24. 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 (1):12
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25. Daeichin V, Akkus Z, Skachkov I, Kooiman K, Needles A, Sluimer J, Janssen B, Daemen MJ, van der Steen AF, de Jong N, Bosch JG. Quantification of bound microbubbles in ultrasound molecular imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2015 Jun; 62 (6):1190-200
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26. Carvalho DD, Akkus Z, van den Oord SC, Schinkel AF, van der Steen AF, Niessen WJ, Bosch JG, Klein S. Lumen segmentation and motion estimation in B-mode and contrast-enhanced ultrasound images of the carotid artery in patients with atherosclerotic plaque. IEEE Trans Med Imaging. 2015 Apr; 34 (4):983-93 Epub 2014 Nov 21
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27. van den Oord SC, Akkus Z, Bosch JG, Hoogi A, ten Kate GL, Renaud G, Sijbrands EJ, Verhagen HJ, van der Lugt A, Adam D, de Jong N, van der Steen AF, Schinkel AF. Quantitative contrast-enhanced ultrasound of intraplaque neovascularization in patients with carotid atherosclerosis. Ultraschall Med. 2015 Apr; 36 (2):154-61 Epub 2014 May 13
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28. Akkus Z, Carvalho DD, van den Oord SC, Schinkel AF, Niessen WJ, de Jong N, van der Steen AF, Klein S, Bosch JG. Fully automated carotid plaque segmentation in combined contrast-enhanced and B-mode ultrasound. Ultrasound Med Biol. 2015 Feb; 41 (2):517-31 Epub 2014 Dec 23
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29. Akkus Z, van Burken G, van den Oord SC, Schinkel AF, de Jong N, van der Steen AF, Bosch JG. Carotid intraplaque neovascularization quantification software (CINQS). IEEE J Biomed Health Inform. 2015 Jan; 19 (1):332-8
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30. van den Oord SC, Akkus Z, Renaud G, Bosch JG, van der Steen AF, Sijbrands EJ, Schinkel AF. 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 Nov; 15 (11):1213-8 Epub 2014 June 27
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31. Carvalho DD, Klein S, Akkus Z, van Dijk AC, Tang H, Selwaness M, Schinkel AF, Bosch JG, van der Lugt A, Niessen WJ. Joint intensity-and-point based registration of free-hand B-mode ultrasound and MRI of the carotid artery. Med Phys. 2014 May; 41 (5):052904
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32. van den Oord SCH, van der Burg J, Akkus Z, Bosch JG, van Domburg RT, Sijbrands EJG, van der Steen AFW, Schinkel AFL. Impact of gender on the density of intraplaque neovascularization: a quantitative contrast-enhanced ultrasound study. Atherosclerosis. 2014 Apr; 233 (2):461-466 Epub 2014 Jan 24
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33. Akkus Z, Hoogi A, Renaud G, van den Oord SC, Ten Kate GL, Schinkel AF, Adam D, de Jong N, van der Steen AF, Bosch JG. New quantification methods for carotid intra-plaque neovascularization using contrast-enhanced ultrasound. Ultrasound Med Biol. 2014 Jan; 40 (1):25-36 Epub 2013 Oct 22
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34. van den Oord SC, Akkus Z, Roeters van Lennep JE, Bosch JG, van der Steen AF, Sijbrands EJ, Schinkel AF. Assessment of subclinical atherosclerosis and intraplaque neovascularization using quantitative contrast-enhanced ultrasound in patients with familial hypercholesterolemia. Atherosclerosis. 2013 Nov; 231 (1):107-13 Epub 2013 Sept 11
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35. van den Oord SC, ten Kate GL, Akkus Z, Renaud G, Sijbrands EJ, ten Cate FJ, van der Lugt A, Bosch JG, de Jong N, van der Steen AF, Schinkel AF. Assessment of subclinical atherosclerosis using contrast-enhanced ultrasound. Eur Heart J Cardiovasc Imaging. 2013 Jan; 14 (1):56-61 Epub 2012 May 29
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36. Hoogi A, Akkus Z, van den Oord SC, ten Kate GL, Schinkel AF, Bosch JG, de Jong N, Adam D, van der Steen AF. Quantitative analysis of ultrasound contrast flow behavior in carotid plaque neovasculature. Ultrasound Med Biol. 2012 Dec; 38 (12):2072-83 Epub 2012 Oct 11
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37. ten Kate GL, Renaud GG, Akkus Z, van den Oord SC, ten Cate FJ, Shamdasani V, Entrekin RR, Sijbrands EJ, de Jong N, Bosch JG, Schinkel AF, van der Steen AF. Far-wall pseudoenhancement during contrast-enhanced ultrasound of the carotid arteries: clinical description and in vitro reproduction. Ultrasound Med Biol. 2012 Apr; 38 (4):593-600 Epub 2012 Feb 15
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38. Carvalho DD, Klein S, Akkus Z, ten Kate GL, Schinkel AF, Bosch JG, van der Lugt A, Niessen WJ. Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound. Int J Comput Assist Radiol Surg. 2012 Mar; 7 (2):207-15 Epub 2011 June 29
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39. Akkus Z, Ramnarine K. V.. Dynamic Assessment of Carotid Plaque Motion Ultrasound. 2010; 18:140-147.