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1. SLIMBRAIN: Augmented reality real-time acquisition and processing system for hyperspectral classification mapping with depth information for in-vivo surgical procedures.

Author

Sancho, Jaime, Villa, Manuel, Chavarrías, Miguel, Juarez, Eduardo, Lagares, Alfonso, and Sanz, César

Subjects
*OPERATIVE surgery, *AUGMENTED reality, *BRAIN tumors, *POINT cloud, *CLASSIFICATION, TUMOR surgery
Abstract

Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations. [ABSTRACT FROM AUTHOR]

Published
2023
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2. A survey of brain tumor segmentation and classification algorithms

Author

Biratu, Erena Siyoum, Schwenker, Friedhelm, Ayano, Yehualashet Megersa, Debelee, Taye Girma, Rundo, Leonardo, Militello, Carmelo, Conti, Vincenzo, Zaccagna, Fulvio, and Han, Changhee

Subjects
Process (engineering), Computer science, Deep learning (Machine learning), Computer applications to medicine. Medical informatics, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, R858-859.7, Brain tumor, Klassifikation, Brain Tumors, Article, shallow machine learning, Photography, medicine, Radiology, Nuclear Medicine and imaging, Segmentation, DDC 000 / Computer science, information & general works, Electrical and Electronic Engineering, TR1-1050, Segmentierung, medicine.diagnostic_test, business.industry, Deep learning, segmentation, deep learning, Pattern recognition, Magnetic resonance imaging, QA75.5-76.95, Hirntumor, medicine.disease, Classification, Computer Graphics and Computer-Aided Design, Statistical classification, ComputingMethodologies_PATTERNRECOGNITION, classification, Region growing, Electronic computers. Computer science, ddc:000, Computer Vision and Pattern Recognition, Artificial intelligence, business, region growing, brain tumor
Abstract

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics., publishedVersion

Published
2021