Polarimetric feature analysis of Mueller matrices for brain tumor image segmentation

Abstract

Mueller matrix polarimetry (MMP) provides valuable structural insights into tissue and holds promise for medical diagnostics. However, its clinical adoption is hindered by labor-intensive data collection and annotation. This study examines the use of MMP data collected in reflection from ex vivo human brain tissue to identify neoplastic regions. Using a custom-built single-wavelength MMP imaging system, we compare deep learning models trained on Mueller matrix measurements against Lu-Chipman feature maps. Our networks achieve segmentation accuracy comparable to multi-spectral polarimetry, highlighting the potential of real-time MMP for brain tumor differentiation. We further provide a qualitative analysis discussing challenges and opportunities for neurosurgical MMP applications.