![]() The specific choices made by different groups differ in terms of target application areas, license, architecture, sustainability and maintenance strategies, and each tool has tradeoffs that should be considered before incorporating it as the basis for development. Nevertheless, several groups have overcome the funding odds and cultural barriers to create tools that have stimulated community building and open science. Traditionally and understandably, reason for the gap between theory and practice is ascribed to the academic reward system and the mechanisms for research funding where novelty is valued over robustness and reproducibility. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today how a well-executed research platform can enable discovery, innovation and reproducible science (“Open Science”) and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. ![]() Critical improvements to the widely used underlying open source libraries and tools - VTK, ITK, CMake, CDash, DCMTK - were an additional consequence of this effort. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. The diffusion tensor imaging technique plays an important role in the evaluation of patients with schizophrenia.The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Conclusion: The volume of hippocampus and the number of fiber bundles were reduced in EGR3 transgenic schizophrenia rats, and are the most sensitive indicators in schizophrenia. A significant reduction in the volume and number of the fiber bundles was also observed in left prefrontal–left hippocampus, left hippocampus–left thalamus, left prefrontal–left hippocampus–left thalamus areas in the model group (all P<0.05). Results: There was a significant decrease in the volume of the fiber beam through the left hippocampus dentate in the schizophrenia model group in comparison to the control group and the risperidone treatment group (P<0.05). The fibronectin in relevant brain regions was also analyzed. MedINRIA software was used for data processing of diffusion tensor and fiber bundles tracking. The volume, quantity, average length of fiber bundles, fractional anisotropy, apparent diffusion coefficient, the relative heterosexual fraction, and volume ratio were collected in the whole brain and schizophrenia related brain areas (the hippocampus, thalamus, and prefrontal lobe). Schizophrenia susceptible brain regions were scanned using in vivo diffusion tensor magnetic resonance imaging. All animals were placed in a fixation system using a commercial rat-dedicated coil. Methods: A rat model of schizophrenia was created by the transfection of the EGR3 gene into rat hippocampus. In this study, we aimed to investigate the white matter connection in schizophrenia susceptible brain regions of early growth response factor 3 (EGR3) expressing rats. White matter fasciculi represent the primary infrastructure for long distance communication in the brain. Background and objective: There is a growing consensus that schizophrenia is ultimately caused by abnormal communication between spatially disparate brain structures.
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