This project aims to develop NeuroManager?, an innovative neuroinformatics platform for advanced parsing, storing, aggregating, analyzing and sharing of complex neuroscience image data. A core technology that we will develop in NeuroManager will be Image Content Analysis for Retrieval Using Semantics (ICARUS), a novel, intelligent neuroimage curation system that will enable image retrieval based on visual appearance or by semantic concept. ICARUS will use machine learning applied to content-based image retrieval - (CBIR) to build and refine models that summarize microscopic and macroscopic image appearance and automatically assign semantic concepts to neuroimages. Neuroscience research generates extensive, multifaceted data that is considerably under-utilized because access to original raw data is typically maintained by the source lab. On the other hand, there are many advantages in sharing complex image data in neuroscience research, including the opportunity for separate analysis of raw data by other scientists from another perspective and improved reproducibility of scientific studies and their results. Unfortunately, none of the neuroscience data sharing options that exist today fulfill all the needs of neuroscientists. To solve this problem, NeuroManager will include the following distinct, significant innovations: (i) versatility for handling two-dimensional (2D) and three-dimensional neuroimaging data sets from animal models and humans; (ii) functionality to share complex datasets that extends secure, privacy-controlled paradigms from institutional, laboratory-based and even public domains; (iii) flexibility to implement NeuroManager within an institute?s IT infrastructure, or on most cloud-based virtualized environments including Azure, Google Cloud Services and Amazon Web Services; (iv) and most importantly, the ICARUS technology for CBIR in neuroimaging data sets. The benefit of NeuroManager for the neuroscience research community, pharmacological and biotechnological R&D, and society in general will be to foster collaboration between scientists and institutions, promoting innovation through combined expertise in an interdisciplinary atmosphere. This will open new horizons for better understanding the neuropathology associated with several human neuropsychiatric and neurological conditions at various levels (i.e., macroscopically, microscopically, subcellularly and functionally), ultimately leading to an improved basis for developing novel treatment and prevention strategies for complex brain diseases. In Phase I we will prove feasibility of this novel technology by developing prototype software that will perform CBIR on 2D whole slide images of coronal sections of entire mouse brains from ongoing research projects of our collaborators. Work in Phase II will focus on developing the commercial software product that will include all of the innovations mentioned above. A competing technology with comparable functionality, addressing the full breadth of needs for modern neuroscience research, is currently not available commercially or otherwise.
There are many advantages in sharing complex image data in neuroscience research, including the opportunity for separate analysis of raw data by other scientists from another perspective and improved reproducibility of scientific studies and their results; however none of the neuroscience data sharing options that exist today fulfill all the needs of neuroscientists. This project commercializes an innovative software for sophisticated advanced parsing, storing, aggregating, analyzing and sharing of complex neuroscience image data, including a novel, intelligent neuroimage curation system that will enable content-based neuroscience image search powered by machine learning, thereby opening new horizons in neuroscience research collaborations. This system will allow researchers to make new discoveries based on new studies that are currently not feasible, ultimately providing the basis for developing novel treatments to prevent and fight complex brain diseases.
