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,tex \chapter{Introduction} Medical imaging and analysis is a quickly growing field that encompasses many different imaging modalities such as X-ray, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Ultrasound. Doctors can use these images to aid diagnosis and choose treatment methods. Engineers work to improve the accuracy and precision of the machines that produce the images, as well as create tools and methods that aid doctors in their analysis. In cancer imaging, PET-CT combines PET images with CT scans to aid doctors with diagnosis by combining a density image (CT scans) and an image showing the locations of a radioactive isotope tracer throughout the body (PET images). Different bodily functions can be measured with different tracers, and for Head and Neck Cancer (HNC), cellular uptake is used. High values of uptake in unusual locations indicate possible cancerous lesions. Because the role of metabolism can vary, a normalization of uptake in lesions based on the uptake in reference areas is often desirable for quantitative analysis. The cerebellum shows a stable tracer uptake, so by measuring it, the responses across the body can be normalized with consistency. This requires the segmentation of the cerebellum. Manual segmentation can be tedious and expensive, so various methods to lessen the need for human input have been attempted such as the work by Bauer et al.~\cite{Bauer}. This research seeks to present an alternative approach that makes use of deep convolutional neural networks (Deep CNNs) which are generally applicable and can be utilized for segmentation and localization tasks.
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