Next Article in Journal
On Computational Aspects of Krawtchouk Polynomials for High Orders
Next Article in Special Issue
Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning
Previous Article in Journal
Practical Camera Sensor Spectral Response and Uncertainty Estimation
Previous Article in Special Issue
Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection
Article

Full 3D Microwave Breast Imaging Using a Deep-Learning Technique

Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
*
Author to whom correspondence should be addressed.
Received: 18 July 2020 / Revised: 3 August 2020 / Accepted: 5 August 2020 / Published: 11 August 2020
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
A deep learning technique to enhance 3D images of the complex-valued permittivity of the breast obtained via microwave imaging is investigated. The developed technique is an extension of one created to enhance 2D images. We employ a 3D Convolutional Neural Network, based on the U-Net architecture, that takes in 3D images obtained using the Contrast-Source Inversion (CSI) method and attempts to produce the true 3D image of the permittivity. The training set consists of 3D CSI images, along with the true numerical phantom images from which the microwave scattered field utilized to create the CSI reconstructions was synthetically generated. Each numerical phantom varies with respect to the size, number, and location of tumors within the fibroglandular region. The reconstructed permittivity images produced by the proposed 3D U-Net show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but it also enhances the detectability of the tumors. We test the trained U-Net with 3D images obtained from experimentally collected microwave data as well as with images obtained synthetically. Significantly, the results illustrate that although the network was trained using only images obtained from synthetic data, it performed well with images obtained from both synthetic and experimental data. Quantitative evaluations are reported using Receiver Operating Characteristics (ROC) curves for the tumor detectability and RMS error for the enhancement of the reconstructions. View Full-Text
Keywords: microwave breast imaging; image reconstruction; tumor detection; convolutional neural networks; deep learning microwave breast imaging; image reconstruction; tumor detection; convolutional neural networks; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Khoshdel, V.; Asefi, M.; Ashraf, A.; LoVetri, J. Full 3D Microwave Breast Imaging Using a Deep-Learning Technique. J. Imaging 2020, 6, 80. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6080080

AMA Style

Khoshdel V, Asefi M, Ashraf A, LoVetri J. Full 3D Microwave Breast Imaging Using a Deep-Learning Technique. Journal of Imaging. 2020; 6(8):80. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6080080

Chicago/Turabian Style

Khoshdel, Vahab; Asefi, Mohammad; Ashraf, Ahmed; LoVetri, Joe. 2020. "Full 3D Microwave Breast Imaging Using a Deep-Learning Technique" J. Imaging 6, no. 8: 80. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6080080

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop