Tissue-Type Classification With Uncertainty Quantification of Microwave and Ultrasound Breast Imaging: A Deep Learning Approach
| Autor: | Joe LoVetri, Vahab Khoshdel, Pedram Mojabi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
uncertainty quantification Computer science Quantitative Biology::Tissues and Organs Physics::Medical Physics Tissue classification convolutional neural network 02 engineering and technology Iterative reconstruction Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine deep learning method 0202 electrical engineering electronic engineering information engineering General Materials Science Uncertainty quantification Pixel business.industry Deep learning Ultrasound General Engineering 020206 networking & telecommunications Pattern recognition ultrasound tomography microwave tomography Ultrasonic sensor lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 182092-182104 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3027805 |
| Popis: | A deep learning approach is proposed for performing tissue-type classification of tomographic microwave and ultrasound property images of the breast. The approach is based on a convolutional neural network (CNN) utilizing the U-net architecture that also quantifies the uncertainty in the classification of each pixel. Quantitative tomographic reconstructions of dielectric properties (complex-valued permittivity), ultrasonic properties (compressibility and attenuation), as well as their combination, with the corresponding actual tissue-type classification constitute the training set. The CNN learns to map the quantitative property reconstructions to a single tissue-type image. The level of confidence in predicting a tissue-type at each pixel is determined. This uncertainty quantification is diagnostically critical for biomedical applications, especially when attempting to distinguish between cancerous and healthy tissues. The Gauss-Newton Inversion algorithm is used for the quantitative reconstruction of both dielectric and ultrasonic properties. Electromagnetic and ultrasound scattered-field data is obtained from MRI-derived numerical breast phantoms. Several numerical breast phantoms types, from fatty to dense, are considered. The proposed classification and uncertainty quantification approach is shown to outperform a previously studied tissue-type classification method based on a Bayesian approach. |
| Databáze: | OpenAIRE |
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