Ozturk, Candide; Eker, Ayse Gul; Pehlivanoglu, Meltem Kurt; Karaduman, Ismet; Olcer, Ercan; Duru, Nevcihan; Dundar, Tolga Turan; Mutluer, Ahmet Serdar; Dogan, Ihsan; Ericek, Omer Burak

Hyperspectral imaging (HSI) is an imaging method that differs from traditional methods by recording the light reflection and absorption properties of each point across a wide color range. HSI enables the non-invasive determination of chemical and structural differences in tissues with high accuracy. In the domain of medicine, hyperspectral imaging represents a novel methodology that facilitates novel approaches in brain surgery, including the distinction of tumors and blood vessels, cancer diagnosis, and the monitoring of tissue oxygen levels and blood flow during surgical procedures. Additionally, hyperspectral imaging has a wide range of applications in various fields, including agriculture, food, environment, geology, and defense.This review comprehensively examines current datasets, application areas, and computational approaches related to the use of hyperspectral imaging technology in brain surgery. HSI enables the precise differentiation of different brain tissues such as tumors, vessels, functional areas, and the spine by collecting data across a wide spectral band range for each pixel. Recent studies in the literature demonstrate that machine learning and deep learning-based methods can achieve high accuracy rates in tissue segmentation and the detection of pathological structures in brain surgery. Additionally, datasets developed at different centers and involving various patient groups provide an important foundation for enhancing the clinical efficacy of HSI.While HSI boasts significant advantages in terms of its non-invasiveness and real-time analysis potential, which benefit surgeons during intraoperative decision-making processes, challenges such as data size, computational costs, and device integration pose barriers to its routine use. In the future, it is anticipated that these limitations will be overcome through the development of efficient algorithms, AI-supported systems, and multi-center validation studies. In conclusion, HSI technology exhibits considerable potential to become a standard intraoperative instrument in neurosurgery, driven by advancing computational methods and increasing clinical validation.

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