Structural Magnetic Resonance Imaging (sMRI) and Diffusion Tensor MRI (DTI) are two common imaging types used in such studies. Magnetic Resonance Imaging (MRI) is a valuable method for analyzing neuro-anatomical structures and connectivity. Although the particular image processing pipelines developed were focused on rodent brain MRI, the presented plugin can be used to support any executable or script-based pipeline. In this paper, we present a description of this innovative Midas plugin, along with results obtained from building and executing several ITK based image processing workflows for diffusion weighted MRI (DW MRI) of rodent brain images, as well as recommendations for building automated image processing pipelines. These capabilities enable reproducible science by sharing datasets and processing pipelines between collaborators. This platform allows simplified troubleshooting, centralized maintenance, and easy data sharing with collaborators. Using this plugin, researchers in collaboration with image processing experts can create workflows with reasonable default settings and streamlined user interfaces, and data can be processed easily from a lab environment without the need for a powerful desktop computer. Current tools work well, but can be inaccessible for those lacking image processing expertise. This represents a new capability for biomedical researchers and offers an innovative platform for scientific collaboration. Pipelines are executed on a grid computing platform using BatchMake and HTCondor. Using this plugin, an image processing expert can construct a pipeline, create a web-based User Interface, manage jobs, and visualize intermediate results.
These tools are implemented as a plugin for Midas, an open-source toolkit for creating web based scientific data storage and processing platforms. In this paper we present a web-based platform that allows an expert to create a brain image processing pipeline, enabling execution of that pipeline even by those biomedical researchers with limited image processing knowledge. Image processing is an important quantitative technique for neuroscience researchers, but difficult for those who lack experience in the field.
Michael Grauer 1 *, Patrick Reynolds 1, Marion Hoogstoel 2, Francois Budin 2, Martin A.
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