Brain tissue analysis is highly desired in neurosurgery, such as tumor resection. To guarantee best life quality afterward, exact navigation within the brain during the surgery is essential. So far, no method has been established that perfectly fulfills this need. Optical coherence tomography (OCT) is a promising three-dimensional imaging tool to support neurosurgical resections. We perform a preliminary study toward in vivo brain tumor removal assistance by investigating meningioma, healthy white, and healthy gray matter. For that purpose, we utilized a commercially available OCT device (Thorlabs Callisto) and measured eight samples of meningioma, three samples of healthy white, and two samples of healthy gray matter ex vivo directly after removal. Structural variations of different tissue types, especially meningioma, can already be seen in the raw OCT images. Nevertheless, an automated differentiation approach is desired, so that neurosurgical guidance can be delivered without a-priori knowledge of the surgeon. Therefore, we employ different algorithms to extract texture features and apply pattern recognition methods for their classification. With these postprocessing steps, an accuracy of nearly 98% was found.

This randomized, double-blind, crossover study examined the effects of a clothing ensemble made of a synthetic fabric promoted as having superior cooling properties (COOL) on exercise performance and its physiological and perceptual determinants during cycle exercise in ambient laboratory conditions that mimic environmental conditions of indoor training/sporting facilities. Twenty athletes (15 men:5 women) aged 25.8 ± 1.2 years (mean ± SEM) with a maximal rate of O2 consumption of 63.7 ± 1.5 mL·kg−1·min−1 completed cycle exercise testing at 85% of their maximal incremental power output to exhaustion while wearing an ensemble consisting of a fitted long-sleeved shirt and full trousers made of either COOL or a synthetic control fabric (CTRL). Exercise endurance time was not different under COOL versus CTRL conditions: 12.38 ± 0.98 versus 11.75 ± 1.10 min, respectively (P > 0.05). Similarly, COOL had no effect on detailed thermoregulatory (skin and esophageal temperatures), cardiometabolic, ventilatory, and perceptual responses to exercise (all P > 0.05). In conclusion, clothing made of a synthetic fabric with purported “cooling” properties did not improve high-intensity cycle exercise endurance in trained athletes under ambient laboratory conditions that mimic the environmental conditions of indoor training/sporting facilities.

Purpose A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images. Methods Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis. Results We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%. Conclusions An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences.

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