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A Real-time imaging and classification system for low-grade glioma detection during brain surgery


Brain tumors remain one of the most incurable illnesses we know; patients have poor prognosis and less than a decade of life expectancy. The oncologist’s toolkit is rather limited as chemotherapy agents have limited access to the brain due to the blood brain barrier selective permeability and radiotherapy results in extensive damage to tissue well beyond the targeted area; leaving surgical removal of the tumor as the most effective approach. It has now become clear that the completeness of the surgical removal of the tumor is the most important factor determining recurrency of the illness. As such, tools that enable neurosurgeons to effectively remove the tumor are of fundamental importance in the surgical theater. Recent advances in intraoperative fluorescent labeling of common brain tumors has proven to dramatically improve surgical outcome. Unfortunately, no such labeling or intraoperative aid exists for the highly infiltrative low-grade glioma (LGG), making it the deadliest type. This PoC project aims to tackle this urgent unmet need and develop an intraoperative, real-time imaging and tissue classification tool for precise on-site identification of LGG tissue. The core technology emanates from the multiphoton ultrafast volumetric imaging we developed (now able to run in real-time) to study the link between neuronal activity and blood flow. Here we harness this unique technology and extended it to perform real-time label-free multimodal acquisition and image processing using the latest deep-learning tools. Our tool will provide surgeons with the ability to discern, while operating, healthy vs tumor tissue. Together with neurosurgeons, a technological hardware partner and an expert in artificial intelligence, we are positioned to generate the first-of-its kind prototype and to seek its commercialization. Such a tool is expected to become a game changer in brain and other tumor surgeries, improving the life expectancy and quality of many patients and families.


Net EU contribution
€ 150 000,00
Ramat Aviv
69978 Tel Aviv

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Activity type
Higher or Secondary Education Establishments
Non-EU contribution
No data