Human Image Database Research Articles

Automated classification of otitis media with OCT: augmenting pediatric image datasets with gold-standard animal model data.

Otitis media (OM) is an extremely common disease that affects children worldwide. Optical coherence tomography (OCT) has emerged as a noninvasive diagnostic tool for OM, which can detect the presence and quantify the properties of middle ear fluid and biofilms. Here, the use of OCT data from the chinchilla, the gold-standard OM model for the human disease, is used to supplement a human image database to produce diagnostically relevant conclusions in a machine learning model. Statistical analysis shows the datatypes are compatible, with a blended-species model reaching ∼95% accuracy and F1 score, maintaining performance while additional human data is collected.

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

SchizConnect: Virtual Data Integration in Neuroimaging.

In many scientific domains, including neuroimaging studies, there is a need to obtain increasingly larger cohorts to achieve the desired statistical power for discovery. However, the economics of imaging studies make it unlikely that any single study or consortia can achieve the desired sample sizes. What is needed is an architecture that can easily incorporate additional studies as they become available. We present such architecture based on a virtual data integration approach, where data remains at the original sources, and is retrieved and harmonized in response to user queries. This is in contrast to approaches that move the data to a central warehouse. We implemented our approach in the SchizConnect system that integrates data from three neuroimaging consortia on Schizophrenia: FBIRN's Human Imaging Database (HID), MRN's Collaborative Imaging and Neuroinformatics System (COINS), and the NUSDAST project at XNAT Central. A portal providing harmonized access to these sources is publicly deployed at schizconnect.org.

Connecting Brain Imaging Terms to Established Lexicons: a Precursor for Data Sharing and Querying

Event Abstract Back to Event Connecting Brain Imaging Terms to Established Lexicons: a Precursor for Data Sharing and Querying Karl Helmer1*, Satrajit Ghosh2, B. Nolan Nichols3, David Keator4, Thomas Nichols5 and Jessica Turner6 1 Massachusetts General Hospital, United States 2 Massachusetts Institute of Technology, United States 3 University of Washington, United States 4 University of California, Irvine, United States 5 University of Warwick, United Kingdom 6 The Mind Institute, United States For data sharing to be useful, data must not only be stored in an organized fashion, but metadata that captures information about how the data was acquired, processed, and analyzed, must also be available. Additionally, metadata must describe data using unambiguously defined terms. Efforts are underway to provide lexicons of brain imaging terms to the neuroscience community. Two examples are NeuroLex[1] and RadLex[2]; lexicons for the domains of neuroscience and radiology, respectively. While NeuroLex follows the OBO[3] principle of always defining a term, RadLex identifies relationships among terms, but often without definitions. On the other side are data and tool repositories which may have a defined schema or a fixed set of terms, but do not provide definitions. Therefore, users may not know precisely what is meant by each term. The lack of standardized terms makes it difficult for query tools to search across collections at different institutions and makes data provenance information ambiguous. The goal of this project is to provide definitions for terms used in each stage of the data lifecycle of brain MRI-based experiments and place the terms within NeuroLex and RadLex. We have begun our efforts with terms used in the acquisition phase. The source of the terms are 1) the XCEDE schema[4], 2) common and private DICOM fields[5], 3) NITRC database query terms[6], and 4) BIRN’s Human Imaging Database (HID) terms[7]. In some cases, connecting terms were added to the lexicons to connect the new set of terms with those already in the lexicons. This work expands existing lexicons with terms commonly used in MRI-based neuroimaging and will enhance the ability to query across data and tool collections. This work was conducted within the Derived Data Task Force of the International Neuroinformatics Coordinating Facility Program on Standards for Datasharing. KGH, DK, and JT are also supported by BIRN (1U24-RR025736, U24-RR021992) and KGH is supported by by the Collaborative Tools Support Network Award (1U24-RR026057-01).

PoseShop: Human Image Database Construction and Personalized Content Synthesis

We present PoseShop--a pipeline to construct segmented human image database with minimal manual intervention. By downloading, analyzing, and filtering massive amounts of human images from the Internet, we achieve a database which contains 400 thousands human figures that are segmented out of their background. The human figures are organized based on action semantic, clothes attributes, and indexed by the shape of their poses. They can be queried using either silhouette sketch or a skeleton to find a given pose. We demonstrate applications for this database for multiframe personalized content synthesis in the form of comic-strips, where the main character is the user or his/her friends. We address the two challenges of such synthesis, namely personalization and consistency over a set of frames, by introducing head swapping and clothes swapping techniques. We also demonstrate an action correlation analysis application to show the usefulness of the database for vision application.

Open Neuroscience Solutions for the Connectome-wide Association Era

The neuroimaging community is at a crossroads. Long characterized by individualism, the data and computational and analytic needs of the connectome-wide association era necessitate cultural reform. Emerging initiatives have demonstrated the feasibility and utility of adopting an open neuroscience model to accelerate the pace and success of scientific discovery.

Neuroscience Data Integration through Mediation: An (F)BIRN Case Study

We describe an application of the BIRN mediator to the integration of neuroscience experimental data sources. The BIRN mediator is a general purpose solution to the problem of providing integrated, semantically-consistent access to biomedical data from multiple, distributed, heterogeneous data sources. The system follows the mediation approach, where the data remains at the sources, providers maintain control of the data, and the integration system retrieves data from the sources in real-time in response to client queries. Our aim with this paper is to illustrate how domain-specific data integration applications can be developed quickly and in a principled way by using our general mediation technology. We describe in detail the integration of two leading, but radically different, experimental neuroscience sources, namely, the human imaging database, a relational database, and the eXtensible neuroimaging archive toolkit, an XML web services system. We discuss the steps, sources of complexity, effort, and time required to build such applications, as well as outline directions of ongoing and future research on biomedical data integration.

Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid.

Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in meaningful ways in support of data analysis, hypothesis testing and future collaborative use is pervasive. Because trans-disciplinary projects rely on effective use of data from many domains, there is a genuine interest in informatics community on how best to store and combine this data while maintaining a high level of data quality and documentation. The difficulties in sharing and combining raw data become amplified after post-processing and/or data analysis in which the new dataset of interest is a function of the original data and may have been collected by multiple collaborating sites. Simple meta-data, documenting which subject and version of data were used for a particular analysis, becomes complicated by the heterogeneity of the collecting sites yet is critically important to the interpretation and reuse of derived results. This manuscript will present a case study of using the XML-Based Clinical Experiment Data Exchange (XCEDE) schema and the Human Imaging Database (HID) in the Biomedical Informatics Research Network's (BIRN) distributed environment to document and exchange derived data. The discussion includes an overview of the data structures used in both the XML and the database representations, insight into the design considerations, and the extensibility of the design to support additional analysis streams.