Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.
Confocal laser endomicroscopy (CLE) is an imaging technique that uses miniaturized fiberoptic probes to allow real-time histological imaging of human tissue. An application of CLE in otorhinolaryngology has hardly been investigated so far. In our study, we analyzed the applicability of CLE to visualize cancerous and healthy tissue of the head and neck region. Formalin-fixed tissue specimens from 135 head and neck squamous cell carcinoma (HNSCC) patients and 50 healthy controls were investigated using CLE with and without topical application of acriflavine. Four head and neck surgeons, four pathologists, and four laymen evaluated the CLE images of the HNSCC cases regarding the tumor localization and its border to healthy tissue. The tumor localization and the tumor border were correctly identified in 97 % by the pathologists, 85 % by the head and neck surgeons, and 70 % by the laymen. The main difference in evaluation results was seen in the correct identification of the tumor site (p < 0.05), while there was no significant difference in the identification of the tumor border. CLE is a valuable tool for real-time histological imaging of HNSCCs. It can help to visualize the tumor border and, thereby, facilitate a more precise tumor surgery.
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