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Zeynettin Akkus
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2020 – today
- 2020
- [j10]Zeynettin Akkus
, Petro M. Kostandy, Kenneth A. Philbrick, Bradley J. Erickson:
Robust brain extraction tool for CT head images. Neurocomputing 392: 189-195 (2020) - [j9]Zeynettin Akkus
Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images. Sensors 20(15): 4175 (2020) - [c6]Mohamed Y. Elwazir, Zeynettin Akkus, Didem Oguz, Zi Ye, Jae K. Oh:
Fully Automated Mitral Inflow Doppler Analysis Using Deep Learning. BIBE 2020: 691-696 - [i3]Mohamed Y. Elwazir, Zeynettin Akkus, Didem Oguz, Jae K. Oh:
Fully Automated Mitral Inflow Doppler Analysis Using Deep Learning. CoRR abs/2011.12429 (2020)
2010 – 2019
- 2019
- [j8]Kenneth A. Philbrick, Alexander D. Weston, Zeynettin Akkus
RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. J. Digit. Imaging 32(4): 571-581 (2019) - [c5]Zeynettin Akkus
Reduction of unnecessary thyroid biopsies using deep learning. Medical Imaging: Image Processing 2019: 109490W - [i2]Tomas Sakinis, Fausto Milletari, Holger Roth, Panagiotis Korfiatis, Petro M. Kostandy, Kenneth Philbrick, Zeynettin Akkus, Ziyue Xu, Daguang Xu, Bradley J. Erickson:
Interactive segmentation of medical images through fully convolutional neural networks. CoRR abs/1903.08205 (2019) - 2018
- [j7]Youngoh Bae, Kunaraj Kumarasamy
Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J. Digit. Imaging 31(2): 252-261 (2018) - [c4]Zeynettin Akkus
Extraction of brain tissue from CT head images using fully convolutional neural networks. Medical Imaging: Image Processing 2018: 1057420 - 2017
- [j6]Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus
Toolkits and Libraries for Deep Learning. J. Digit. Imaging 30(4): 400-405 (2017) - [j5]Zeynettin Akkus
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J. Digit. Imaging 30(4): 449-459 (2017) - [j4]Zeynettin Akkus
Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. J. Digit. Imaging 30(4): 469-476 (2017) - 2016
- [i1]Zeynettin Akkus, Issa Ali, Jirí Sedlár, Timothy L. Kline, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, Bradley J. Erickson:
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning. CoRR abs/1611.06939 (2016) - 2015
- [j3]Zeynettin Akkus
Carotid Intraplaque Neovascularization Quantification Software (CINQS). IEEE J. Biomed. Health Informatics 19(1): 332-338 (2015) - [j2]Diego D. B. Carvalho, Zeynettin Akkus
Lumen Segmentation and Motion Estimation in B-Mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients With Atherosclerotic Plaque. IEEE Trans. Medical Imaging 34(4): 983-993 (2015) - 2014
- [c3]Diego D. B. Carvalho, Zeynettin Akkus
Nonrigid motion compensation in B-mode and contrast enhanced ultrasound image sequences of the carotid artery. Medical Imaging: Image Processing 2014: 90340N - [c2]Zeynettin Akkus
Atherosclerotic carotid lumen segmentation in combined B-mode and contrast enhanced ultrasound images. Medical Imaging: Image Processing 2014: 903445 - 2012
- [j1]Diego D. B. Carvalho, Stefan Klein
Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound. Int. J. Comput. Assist. Radiol. Surg. 7(2): 207-215 (2012) - [c1]Diego D. B. Carvalho, Stefan Klein
Registration of Free-Hand Ultrasound and MRI of Carotid Arteries through Combination of Point-Based and Intensity-Based Algorithms. WBIR 2012: 131-140
Coauthor Index
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