The Biomedical Image Analysis and Services Section (BIRSS) is committed to providing computational and engineering expertise to a variety of clinical and biomedical informatics activities at NIH. Specifically, biomedical imaging research in PET, ultrasound, CT, MRI, microscopy, cancer research, and neural dysfunction have been supported extensively. To advance and empower scientific research in the NIH intramural program, CIT has developed and continues to enhance a sophisticated open source, platform-independent, n-dimensional, extensible image processing and visualization application. The MIPAV (Medical Image Processing Analysis and Visualization) (http://mipav.cit.nih.gov/) is an application that enables quantitative analysis and visualization of biomedical imaging modalities (from micro to macro) and is used by researchers at NIH and around the world. At NIH, MIPAV has been used to analyze anatomical structures in CT datasets, analysis of MRI datasets for NIMH, and has been used by NCI for the analysis of 2D and 3D microscopic samples. In addition, BIRSS leads the development of the Biomedical Research Informatics Computing System (BRICS) (http://brics.cit.nih.gov/) which is a collaborative and extensible web-based system to support the collection and analysis of research studies and clinical trials, using a set of modular components that cover all stages of the research life cycle. And because BRICS is un-branded and not associated with a particular disease or organization, it can be efficiently custom-tailored for many research programs. MIPAVs integrated set of biomedical imaging algorithms and its extensibility have been used by BIRSS to implement solutions to imaging problems in the NIH intramural research community dozens of times over. To create custom workflows and solutions for intramural collaborators, BIRSS team members can build plug-ins that leverage the algorithms and tools in MIPAV to solve complex imaging research questions. For example, BIRSS has created a novel MIPAV plug-in as part of a collaboration with Dr. Hari Shroffs lab in the National Institute of Biomedical Imaging and Bioengineering (NIBIB) to untwist four-dimensional high-resolution microscopy images of the Caenorhabditis elegans nematode embryo throughout its development. This plug-in used the image registration and visualization tools already developed by BIRSS for MIPAV, along with novel fiducial annotation and lattice warping tools, to allow the NIBIB researchers to annotate and regularize the C. elegans embryo data through its twitching phase of development, which has not previously been possible algorithmically. This, in turn, allowed Dr. Shroffs group to investigate neurodevelopmental events in late embryogenesis and apply it to track the 3D positions of seam cell nuclei, neurons, and neurites in multiple elongating embryos. The detailed positional information obtained enabled NIBIB to develop a composite model showing movement of these cells and neurites in an 'average' worm embryo. The untwisting and cell tracking capabilities of this plug-in provides a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis. Accurate automatic organ segmentation is an important yet challenging task for medical image analysis. Anatomical variability in shape and texture feature inhibits traditional segmentation methods from achieving high accuracies. Machine learning has dominated the medical imaging research field in the past decade. Initially, pioneer work with decent feature extraction and SVM based image classification achieves better results. Later, learning based detection algorithm began to dominate the machine learning tools like boosting trees, random forest. More recently the deep learning based Deep Convolutional Neural Networks (DCNNs) become the mainstream of the medical imaging research field for the past two years. Using and enhancing the MIPAV application has allowed us to rapidly build the new machine learning component integral to the MIPAV software and is being used to support automated segmentation of the prostate. BIRSS also leads the development of the Biomedical Research Informatics Computing System (BRICS) (http://brics.cit.nih.gov/) which is a collaborative and extensible web-based informatics system to support the collection and analysis of research studies and clinical trials. BRICS is un-branded and not associated with a particular disease or organization, therefore, it can be efficiently custom-tailored for many research programs. For example, in collaboration with the National Institute of Neurological Disorders and Stroke (NINDS), BIRSS has developed two informatics systems, using the BRICS system, in support of Traumatic Brain Injury (TBI)(http://fitbir.nih.gov/)research, the Parkinsons Disease Biomarker Program (PDBP) (http://pdbp.ninds.nih.gov/), as well as, collaborated with the National Eye Institute developed an informatics system for rare eye diseases, eyeGENE (https://eyegene.nih.gov/). The TBI informatics system is called the Federal Interagency TBI Research (FITBIR) database to acknowledge the interagency participation and shared interests. FITBIR serves as a repository for TBI research, is supported by multiple federal agencies, and consolidates high quality, uniformly collected, and contemporary data that can be accessed and analyzed by scientific experts. Over one million records have been uploaded to FITBIR thus far for 24,545 subjects enrolled in 45 different research studies. Currently there are 105 studies expected to contribute to FITBIR and the number of studies is growing every year. Within FITBIR are clinical outcome data and imaging data of which 34,883 records are of imaging data (MRI, CT, PET and Diffusion) from 2,616 individual subjects. Of that data, FITBIR contains 13,069 rows of imaging data from 1,794 individual subjects that are shared with users who have approved Data Access accounts. A summary of the data can be found here: https://fitbir.nih.gov/content/submitted-data. The goal of the PDBP, a BRICS system, is to support new and existing research and resource development promoting biomarker discovery for Parkinson's disease. Although our understanding of the biology and genetics associated with Parkinson's disease (PD) is advancing rapidly, gaps remain between promising laboratory discoveries and the realization of treatments that will cure or slow progression of PD. To address the needs of the PD community, NINDS has established the PDBP program focused on promoting the discovery of biomarker candidates for early detection and measurement of disease progression. To date, the PDBP prospective consortium has 100% accrual at nine sites across the US with 1,536 enrolled subjects of which biorepository samples have been collected from 1,501 subjects. The total number of unique biospecimens collected longitudinally across all sites totals 15,524. 1,444 DNA samples 3,854 RNA samples 3,255 plasma samples 2,934 serum samples 787 CSF samples 3,250 whole blood samples A summary of the data can be found here: https://pdbp.ninds.nih.gov/Data The National Eye Institute (NEI) has also adopted the BRICS system to support The National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE) (https://eyegene.nih.gov/). The eyeGENE project is a research venture created by NEI in response to promising scientific discoveries in genetics. eyeGENE aims to advance studies of eye diseases and their genetic causes by giving researchers access to DNA samples, clinical information, and patients looking to participate in research studies and clinical trials. A summary of the data can be found here: https://eyegene.nih.gov/node/35.
| Price, Michelle A; Bixby, Pam J; Phillips, Monica J et al. (2018) Launch of the National Trauma Research Repository coincides with new data sharing requirements. Trauma Surg Acute Care Open 3:e000193 |
| Ting, Chun-Yuan; McQueen, Philip G; Pandya, Nishith et al. (2017) Analyzing Dendritic Morphology in Columns and Layers. J Vis Exp : |
| Chen, Xianlai; Fann, Yang C; McAuliffe, Matthew et al. (2017) Checking Questionable Entry of Personally Identifiable Information Encrypted by One-Way Hash Transformation. JMIR Med Inform 5:e2 |
| Cheng, Ruida; Roth, Holger R; Lay, Nathan et al. (2017) Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham) 4:041302 |
| Rosenthal, Liana S; Drake, Daniel; Alcalay, Roy N et al. (2016) The NINDS Parkinson's disease biomarkers program. Mov Disord 31:915-23 |
| Fu, Yan; Winter, Peter W; Rojas, Raul et al. (2016) Axial superresolution via multiangle TIRF microscopy with sequential imaging and photobleaching. Proc Natl Acad Sci U S A 113:4368-73 |
| Christensen, Ryan Patrick; Bokinsky, Alexandra; Santella, Anthony et al. (2015) Untwisting the Caenorhabditis elegans embryo. Elife 4: |
| Johnson, Stephen B; Whitney, Glen; McAuliffe, Matthew et al. (2010) Using global unique identifiers to link autism collections. J Am Med Inform Assoc 17:689-95 |
| Giesel, F L; Mehndiratta, A; Locklin, J et al. (2009) Image fusion using CT, MRI and PET for treatment planning, navigation and follow up in percutaneous RFA. Exp Oncol 31:106-14 |
| Calabrese, Massimiliano; De Stefano, Nicola; Atzori, Matteo et al. (2008) Extensive cortical inflammation is associated with epilepsy in multiple sclerosis. J Neurol 255:581-6 |
