Exploration of Realtime Brain tumor classification from Hyperspectral Images in Heterogeneous Embedded MPSoC

To reduce the frequency of relapses after surgical removal a brain tumor, it is critically important to completely remove all affected areas of the brain without disrupting the functionality of vital organs. Therefore, intraoperative differential diagnostics of micro-areas of tumor tissue with their subsequent removal or destruction is an urgent task that determines the success of the operation as a whole. Optical spectroscopy has shown its advantages over the past decade when used as a tool for intraoperative metabolic navigation. And one of the most promising options for the development of this technology is spectrally-resolved imaging. Currently, methods of spectrally-resolved imaging in diffusely reflected light have been developed, for example, mapping the degree of hemoglobin oxygen saturation, as well as fluorescence visualization systems, for both endogenous fluorophores and special fluorescent markers. These systems allow rapid analysis of tissue by the composition of chromophores and fluorophores, which allows the neurosurgeon to differentiate tumor and normal tissues, as well as functionally significant areas, during surgery. No less mandatory are the methods of using spectrally resolved visualization based on mapping characteristics obtained from Raman spectra, but due to the smaller cross-section of the process, these methods are used ex vivo, as a rule, for urgent analysis of fresh tissue samples. In this paper, we focus on both the physical foundations of such methods and a very important aspect of their application – machine learning (ML) methods for image processing and tissues’ classification.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Exploration of Realtime Brain tumor classification from Hyperspectral Images in Heterogeneous Embedded MPSoC

Cited by 2 publications

References 11 publications

FPGA-based parallel implementation to classify Hyperspectral images by using a Convolutional Neural Network

FPGA-based parallel implementation to classify Hyperspectral images by using a Convolutional Neural Network

Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology

Contact Info

Product

Resources

About