This study aims to integrate real-time hyperspectral (HS) imaging with a surgical microscope to assist neurosurgeons in differentiating between healthy and pathological tissue during procedures. Using the LEICA M525 microscope’s optical ports, we register HS data and RGB, in an efforts to improve margin delineation and surgical outcomes. The CUBERT ULTRIS SR5 camera with 51 bands and 15 Hz is employed, and critical calibration steps are outlined for clinical application. Experimental validation is conducted on ex-vivo animal tissue using reflectance spectroscopy. We present the preliminary validation results of the performance comparison between the designed hyperspectral imaging microscope prototype and diffuse reflectance spectroscopy conducted on animal tissue.
This research aims to deal with intraoperative multispectral images taken from brain tumour surgeries to investigate the diagnostic and guidance potential of MSI. These images were registered by feature-based (SIFT, PFN), intensity-based (LK) and machine learning (RANSAC-Flow) methods and classified via a CNN and Transformer model using anatomical labels. Based on the results from some initial training, MSI could achieve 95% overall accuracy. After labelling and registration are completed, a brain surgery dataset can be built to support intraoperative decision making.
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