Borghouts Marijn, Ambrosanio Michele, Franceschini Stefano, Autorino Maria Maddalena, Pascazio Vito, Baselice Fabio
Department of Biomedical Engineering, Technical University of Eindhoven, 5600 MB Eindhoven, The Netherlands.
Department of Economics, Law, Cybersecurity and Sports Sciences, University of Naples "Parthenope", 80035 Nola, Italy.
Bioengineering (Basel). 2023 Oct 2;10(10):1153. doi: 10.3390/bioengineering10101153.
microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability.
this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail.
the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model's ability to accurately locate the core of a single tumor (within 0.9 cm for most cases).
overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.
微波成像（MWI）已成为一种有前景的乳腺癌筛查方式，可提供经济高效、快速、安全且舒适的检查。然而，MWI在肿瘤检测和定位方面的实际应用受到其固有的低分辨率和低检测能力的阻碍。
本研究旨在直接从散射矩阵生成准确的肿瘤概率图。这种直接转换使概率图独立于特定的图像形成技术，因此可能与任何图像形成技术互补。一种基于卷积神经网络（CNN）的方法被用于将散射矩阵转换为肿瘤概率图。所提出的深度学习模型使用二维（2D）乳腺切片的大型真实数值数据集进行训练。通过视觉检查和定量测量来评估模型在不同细节水平下的预测质量，从而评估模型的性能。
结果表明，在将轮廓分类为健康或患病方面具有非常高的准确率（0.9995），并展示了模型准确定位单个肿瘤核心的能力（大多数情况下在0.9厘米范围内）。
总体而言，本研究表明，基于神经网络（NN）从散射矩阵直接转换为肿瘤概率图的方法在推进MWI领域的先进肿瘤检测算法方面具有前景。
