ArticleJournal of biomedical optics2021
Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall.
Article in Journal of biomedical optics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Multi-layer self-calibrated algorithm for transabdominal fetal pulse oximetry: simulation andJPhys photonics · 2026Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- In vivo optimization of the experimental conditions for the non-invasive optical assessment of breast density.Scientific reports · 2024Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Physics-guided neural network for tissue optical properties estimation.Biomedical optics express · 2023Article
- Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.Biomedical optics express · 2023Article
- Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming.Bioengineering (Basel, Switzerland) · 2023Article
- Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data.Journal of biomedical optics · 2023Article
- Deep Learning for Type 1 Diabetes Mellitus Diagnosis Using Infrared Quantum Cascade Laser Spectroscopy.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
significanceIn general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.
aimWe aim to reduce the chest wall's effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.
resultsThe CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.
conclusionsOur proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.
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