scholarly journals A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 674
Author(s):  
Keeley Edwards ◽  
Vahab Khoshdel ◽  
Mohammad Asefi ◽  
Joe LoVetri ◽  
Colin Gilmore ◽  
...  
Keyword(s):  
Prior Information ◽  
Measurement Data ◽  
Source Inversion ◽  
Parameter Inference ◽  
Contrast Source Inversion ◽  
Bulk Parameters ◽  
Bulk Parameter ◽  
Stage 1 ◽  
Tissue Geometry ◽  
Complex Valued

A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

scholarly journals Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4050 ◽  
Author(s):  
Vahab Khoshdel ◽  
Ahmed Ashraf ◽  
Joe LoVetri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Breast Imaging ◽  
Reconstruction Error ◽  
Tumor Segmentation ◽  
Source Inversion ◽  
The Neural Network ◽  
Contrast Source Inversion ◽  
Imaging Artifacts ◽  
Complex Valued

We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) technique is used to create the complex-permittivity images of the breast with ultrasound-derived tissue regions utilized as prior information. However, imaging artifacts make the detection of tumors difficult. To overcome this issue we train a convolutional neural network (CNN) that takes in, as input, the dual-modal CSI reconstruction and attempts to produce the true image of the complex tissue permittivity. The neural network consists of successive convolutional and downsampling layers, followed by successive deconvolutional and upsampling layers based on the U-Net architecture. To train the neural network, the input-output pairs consist of CSI’s dual-modal reconstructions, along with the true numerical phantom images from which the microwave scattered field was synthetically generated. The reconstructed permittivity images produced by the CNN show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but can also improve the detectability of tumors. The performance of the CNN is assessed using a four-fold cross-validation on our dataset that shows improvement over CSI both in terms of reconstruction error and tumor segmentation performance.

Finite-element contrast source inversion method for microwave imaging

2010 ◽  
Vol 26 (11) ◽  
pp. 115010 ◽  
Author(s):  
Amer Zakaria ◽  
Colin Gilmore ◽  
Joe LoVetri
Keyword(s):  
Finite Element ◽  
Microwave Imaging ◽  
Inversion Method ◽  
Source Inversion ◽  
Contrast Source Inversion

A wavelet-based contrast source inversion method

2021 ◽  
Author(s):  
Yarui Zhang ◽  
Marc Lambert ◽  
Aurelia Fraysse ◽  
Dominique Lesselier
Keyword(s):  
Inversion Method ◽  
Source Inversion ◽  
Contrast Source Inversion
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