Abstract
Advances in medical image applications have led to mounting expectations in regard to their impact on neuroscience studies. In light of this fact, a comprehensive application is needed to move neuroimaging data into clinical research discoveries in a way that maximizes collected data utilization and minimizes the development costs. We introduce BRAINS AutoWorkup, a Nipype based open source MRI analysis application distributed with BRAINSTools suite (http://brainsia.github.io/BRAINSTools/). This work describes the use of efficient and extensible automated brain MRI analysis workflow for large-scale multi-center longitudinal studies. We first explain benefits of our extensible workflow development using Nipype, including fast integration and validation of recently introduced tools with heterogeneous software infrastructures. Based on this workflow development, we also discuss our recent advancements to the workflow for reliable and accurate analysis of multi-center longitudinal data. In addition to Nipype providing a unified workflow, its support for High Performance Computing (HPC) resources leads to a further increased time efficiency of our workflow. We show our success on a few selected large-scale studies, and discuss future direction of this translation research in medical imaging applications.
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Notes
- 1.
We understand that a reference template and atlas are often used interchangeably. To avoid the confusion of terminology in this paper, we use template for a set of MRI images inlcuding tissue probability priors for tissue classification and atlas for a set of MRI image including reference structural segmentation for multi-atlas labeling method.
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Acknowledgments
We wish to express our gratitude to SINAPSE team members for their all support. This research was supported by HDSA Develope Segmentation Pipeline, NIH Neurobiological Predictors of Huntington’s Disease (PREDICT-HD; NS40068, NS050568) and Brains Morphology and Image Analysis (1R01NS050568-01A2), National Alliance for Medical Image Computing (NAMIC; EB005149/Brigham and Women’s Hospital), Enterprise Storage in a Collaborative Neuroimaging Environment (S10 RR023392/NCCR Shared Instrumentation Grant).
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Kim, R.E., Nopoulos, P., Paulsen, J., Johnson, H. (2016). Efficient and Extensible Workflow: Reliable Whole Brain Segmentation for Large-Scale, Multi-center Longitudinal Human MRI Analysis Using High Performance/Throughput Computing Resources. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2015. Lecture Notes in Computer Science(), vol 9401. Springer, Cham. https://doi.org/10.1007/978-3-319-31808-0_7
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