A Neurosurgical Instrument Segmentation Approach to Assess Microsurgical Movements.

The ability to recognize anatomical landmarks, microsurgical instruments, and complex scenes and events in a surgical wound using computer vision presents new opportunities for studying microsurgery effectiveness. In this study, we aimed to develop an artificial intelligence-based solution for detecting, segmenting, and tracking microinstruments using a neurosurgical microscope. We have developed a technique to process videos from microscope camera, which involves creating a segmentation mask for the instrument and subsequently tracking it.

MR-guided non-invasive typing of brain gliomas using machine learning.

Unlabelled: Gliomas are the most common neuroepithelial brain tumors. The modern classification of tumors of central nervous system and treatment approaches are based on tissue and molecular features of a particular neoplasm. Today, histological and molecular genetic typing of tumors can only be carried out through invasive procedures.

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molecular diagnostics for nervous system neoplasms can revolutionize the efficacy and safety of medical care and radically reduce healthcare costs.