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Ioannis Sechopoulos
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2020 – today
- 2023
- [c19]Sjoerd A. M. Tunissen, Andrea Motta, Franziska Mauter
, Eloy García, Oliver Díaz, John M. Boone, Ioannis Sechopoulos, Marco Caballo:
End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms. Computer-Aided Diagnosis 2023 - [c18]Kirsten L. Boedeker, Daniel W. Shin, Luuk J. Oostveen, Ioannis Sechopoulos, Craig K. Abbey:
Discriminability of non-Gaussian noise properties in CT. Image Perception, Observer Performance, and Technology Assessment 2023 - 2022
- [c17]Mikhail Mikerov, Koen Michielsen, Nikita Moriakov, Ioannis Sechopoulos
Adding patient motion from DCE-MRI to anthropomorphic phantoms for dedicated breast CT. Image Processing 2022 - [c16]Sarah D. Verboom
Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors. Image Processing 2022 - [c15]Luuk J. Oostveen, Kirsten L. Boedeker, Daniel W. Shin, Craig K. Abbey, Ioannis Sechopoulos
Visibility of noise texture changes in CT images. Image Perception, Observer Performance, and Technology Assessment 2022 - [i3]Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi, Jonas Teuwen, Ioannis Sechopoulos
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging. CoRR abs/2205.14147 (2022) - 2021
- [j5]Jonas Teuwen, Nikita Moriakov
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation. Medical Image Anal. 71: 102061 (2021) - 2020
- [j4]Marco Caballo, Domenico R. Pangallo, Ritse M. Mann, Ioannis Sechopoulos
Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence. Comput. Biol. Medicine 118: 103629 (2020) - [j3]Floris Heutink, Valentin Koch, Berit Verbist, Willem Jan van der Woude
Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images. Comput. Methods Programs Biomed. 191: 105387 (2020) - [c14]Craig K. Abbey, Michael A. Webster, Tanya D. Geertse, Danielle van der Waal, Eric Tetteroo, Ruud Pijnappel, Mireille J. M. Broeders, Ioannis Sechopoulos
Sequential reading effects in Dutch screening mammography. Image Perception, Observer Performance, and Technology Assessment 2020: 113160G - [i2]Jonas Teuwen, Nikita Moriakov, Christian Fedon, Marco Caballo, Ingrid S. Reiser, Predrag R. Bakic, Eloy García, Oliver Díaz, Koen Michielsen, Ioannis Sechopoulos:
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation. CoRR abs/2006.06508 (2020)
2010 – 2019
- 2019
- [c13]Marco Caballo, Jonas Teuwen, Ritse Mann, Ioannis Sechopoulos
Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images. Computer-Aided Diagnosis 2019: 109500J - [c12]Christiana Balta, Ioannis Sechopoulos
New difference of Gaussian channel-sets for the channelized Hotelling observer? Image Perception, Observer Performance, and Technology Assessment 2019: 109520C - [c11]Christiana Balta, Ioannis Sechopoulos
2D single-slice vs. 3D viewing of simulated tomosynthesis images of a small-scale breast tissue model. Image Perception, Observer Performance, and Technology Assessment 2019: 109520V - 2018
- [c10]Joana Boita, Alistair Mackenzie
Breast phantom validation of a mammographic image modification method. IWBI 2018: 107180P - [c9]Koen Michielsen
Dose reduction in breast CT by spectrum switching. IWBI 2018: 107180J - [c8]Ramona W. Bouwman, Christiana Balta, David R. Dance, Kenneth C. Young, Alistair Mackenzie
Independent images: a need for phantom based image quality assessment using model observers? IWBI 2018: 107181N - [c7]Marco Caballo, Christian Fedon
Automatic estimation of glandular tissue loss due to limited reconstruction voxel size in tomographic images of the breast. IWBI 2018: 107181G - [c6]Alejandro Rodríguez-Ruiz, Ruben E. van Engen, Koen Michielsen
How does wide-angle breast tomosynthesis depict calcifications in comparison to digital mammography? A retrospective observer study. IWBI 2018: 107181T - [c5]Alejandro Rodríguez-Ruiz, Jan-Jurre Mordang, Nico Karssemeijer, Ioannis Sechopoulos
Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support? IWBI 2018: 1071803 - [c4]Alejandro Rodríguez-Ruiz, Jonas Teuwen, Kaman Chung, Nico Karssemeijer, Margarita Chevalier, Albert Gubern-Mérida, Ioannis Sechopoulos
Pectoral muscle segmentation in breast tomosynthesis with deep learning. Computer-Aided Diagnosis 2018: 105752J - [i1]Nikita Moriakov, Koen Michielsen
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction. CoRR abs/1808.04640 (2018) - 2016
- [c3]Katharina Holland, Ioannis Sechopoulos
Performance of Breast Cancer Screening Depends on Mammographic Compression. Digital Mammography / IWDM 2016: 183-189 - 2014
- [c2]Xulei Qin, Guolan Lu, Ioannis Sechopoulos
Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features. Image Processing 2014: 90341V - 2013
- [j2]Veronica Mejia Bustamante, James G. Nagy, Steve S. J. Feng, Ioannis Sechopoulos
Iterative Breast Tomosynthesis Image Reconstruction. SIAM J. Sci. Comput. 35(5) (2013) - 2011
- [c1]Xiaofeng Yang
Automatic tissue classification for high-resolution breast CT images based on bilateral filtering. Image Processing 2011: 79623H - 2010
- [j1]Julianne Chung
Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction. SIAM J. Imaging Sci. 3(1): 133-152 (2010)
Coauthor Index
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