  Neuro-oncological surgery is the primary brain cancer treatment, yet it faces
challenges with gliomas due to their invasiveness and the need to preserve
neurological function. Hence, radical resection is often unfeasible,
highlighting the importance of precise tumor margin delineation to prevent
neurological deficits and improve prognosis. Imaging Mueller polarimetry, an
effective modality in various organ tissues, seems a promising approach for
tumor delineation in neurosurgery. To further assess its use, we characterized
the polarimetric properties by analysing 45 polarimetric measurements of 27
fresh brain tumor samples, including different tumor types with a strong focus
on gliomas. Our study integrates a wide-field imaging Mueller polarimetric
system and a novel neuropathology protocol, correlating polarimetric and
histological data for accurate tissue identification. An image processing
pipeline facilitated the alignment and overlay of polarimetric images and
histological masks. Variations in depolarization values were observed for grey
and white matter of brain tumor tissue, while differences in linear retardance
were seen only within white matter of brain tumor tissue. Notably, we
identified pronounced optical axis azimuth randomization within tumor regions.
This study lays the foundation for machine learning-based brain tumor
segmentation algorithms using polarimetric data, facilitating intraoperative
diagnosis and decision making.
