Driven segmentation of synthetic microwave images for breast cancer detection

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, continuing to be the leading cause of cancer-related mortality among women. Current global health statistics reveal that millions of new breast cancer cases are diagnosed annually, with a disproportionate concentration in low- and middle-income countries (LMICs). In these regions, disparities in healthcare infrastructure, socioeconomic challenges, and limited access to advanced diagnostic technologies substantially hinder early detection efforts. Early diagnosis of breast cancer is of paramount importance, as timely intervention significantly improves patient survival rates, reduces the need for aggressive treatments, mitigates treatment-related side effects, and ultimately decreases the economic burden on healthcare systems. Despite technological advancements, conventional breast imaging modalities such as mammography, ultrasound, and magnetic resonance imaging (MRI) still face notable limitations globally in terms of accessibility, cost, and diagnostic efficacy. Mammography, regarded as the clinical gold standard for breast cancer screening, involves ionizing radiation and poses potential health risks with frequent use. Moreover, its diagnostic sensitivity is considerably reduced in women with dense breast tissue, leading to false-negative results and delayed treatment. Radiation-free ultrasound imaging requires highly skilled operators, resulting in variability in diagnostic accuracy and limiting its widespread application. Nuclear imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) provide valuable dynamic and functional imaging data; however, their high costs, radiation exposure, and limited availability restrict their broader clinical use. MRI offers superior soft tissue contrast and spatial resolution but is associated with high operational costs, infrastructure demands, and limited accessibility, especially in LMICs. Collectively, these challenges underscore the urgent need for safe, cost-effective, scalable, and reliable novel imaging technologies for early tumor detection. Microwave Imaging (MWI) has recently emerged as a promising alternative modality that enables non-ionizing electromagnetic wave-based examination of the internal structural and functional properties of breast tissue. The fundamental principle of MWI relies on the dielectric property differences—specifically dielectric permittivity (∈_r) and electrical conductivity (σ)—between malignant tumors and normal breast tissue. This contrast provides functional imaging information complementary to the anatomical data obtained from conventional methods. Microwave imaging systems primarily consist of transmitting and receiving antennas, with the collected data computationally reconstructed into images. Additionally, MWI systems are inherently more affordable, portable, and easier to operate, making them highly suitable for resource-constrained clinical environments. Nevertheless, the clinical translation of MWI has been impeded by intrinsic limitations such as relatively low spatial resolution, sensitivity to noise and artifacts, and challenges in accurately localizing tumors within reconstructed images. In response to these challenges, the present thesis proposes a comprehensive deep learning framework aimed at enhancing the diagnostic performance and tumor localization accuracy of microwave breast imaging. Within this scope, synthetic breast tissue models were developed using a high-fidelity simulation platform based on the Reverse Time Migration (RTM) algorithm, which realistically models the propagation and scattering of electromagnetic waves in heterogeneous and homogeneous biological tissues. The RTM algorithm processes source and receiver wavefields bidirectionally and establishes correlations between them to generate an image. It consists of two steps involving the backpropagation of complex conjugate data into the imaging domain and computing the cross-correlation norm of the forward and backward propagated fields. Synthetic breast models were generated employing microwave difference imaging based on the total electric field, necessitating knowledge of the healthy breast state during imaging. The total electric field data required for microwave imaging were synthetically produced via numerical solutions of electromagnetic scattering problems. This study investigates deep learning-based segmentation of low-resolution microwave images generated from total electric field data. The dataset comprises 1000 synthetic transverse breast images, each containing a single tumor, initially created at a high resolution of 400×400 pixels.