Deep Learning Algorithms for FreeSurfer

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Abstract FreeSurfer is a tool for the analysis of Magnetic Resonance Imaging (MRI) that has proven to be a flexible and powerful technology for quantifying the effects of many conditions, including numerous neurological disorders, on human brain anatomy, connectivity, vasculature, chemical composition, physiology and function. In the past 20 years, these open source tools have been developed to accurately and automatically segment an array of brain structures and have become the core analysis infrastructure for the Alzheimer’s Disease NeuroImaging Initiative (ADNI). In this project, we seek the resources to radically increase the speed, accuracy and flexibility of these tools, taking advantage of exciting new results in Deep Learning. This will enable us to more accurately quantify neuroanatomical changes that are critical to diagnosing, staging and assessing the efficacy of potential therapeutic interventions in diseases such as Alzheimer’s. This includes the generation of documentation, tutorials, unit tests, regression tests and system tests to harden the tools and make them usable by clinicians and neuroscientists, and finally the distribution and support of the data, manual labelings and tools to the more than 40,000 researchers that use FreeSurfer through our existing open source mechanism. In addition, we will analyze the entire Alzheimer’s Disease NeuroImaging Initiative dataset and return it for public release, including a set of manually labeled data that can be used to optimize Deep Learning tools for Alzheimer’s Disease over the next decade.