ABSTRACT
Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to bolster an existing image classification dataset.
ABSTRACT
The importance of preserving function during glioma surgery cannot be overemphasized. There are a number of techniques utilized including functional MRI, direct electrophysiological monitoring and functional neuronavigation to maximize and safely resect gliomas. The intraoperative MRI is an extremely valuable tool and adds to the neurosurgeon's armamentarium. Combining these technologies will enhance the safety and efficacy of glioma surgery. This is the first report in the literature where we successfully combine both functional neuronavigation and subcortical stimulation, using a single probe to safely resect a recurrent glioblastoma.
