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.
Hahne et al. (Mon,) studied this question.