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Bajcsy, P.

Publications and source records attributed to Bajcsy, P..

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Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

1 -Digital light microscopy provides powerful tools for quantitatively probing the real-time dynamics of subcellular structures. While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). Keeping notes on microscopy experiments and quality control procedures ought to be straightforward, as the microscope is a machine whose components are defined and the performance measurable. Nevertheless, to this date, no universally adopted community-driven specifications exist that delineate the required information about the microscope hardware and acquisition settings (i.e., microscopy "data provenance" metadata) and the minimally accepted calibration metrics (i.e., microscopy quality control metadata) that should be automatically recorded by both commercial microscope manufacturers and customized microscope developers. In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To add to the confusion, microscopy experiments vary greatly in aim and complexity, ranging from purely descriptive work to complex, quantitative and even sub-resolution studies that require more detailed reporting and quality control measures. To solve this problem, the 4D Nucleome Initiative (4DN) (1, 2) Imaging Standards Working Group (IWG), working in conjunction with the BioImaging North America (BINA) Quality Control and Data Management Working Group (QC-DM-WG) (3), here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a revision of the Core of the Open Microscopy Environment (OME) Data Model, which forms the basis for the widely adopted Bio-Formats library (4-6), accompanied by a suite of three extensions, each with three tiers, allowing the classification of imaging experiments into levels of increasing imaging and analytical complexity (7, 8). Hence these specifications not only provide an OME-based comprehensive set of metadata elements that should be recorded, but they also specify which subset of the full list should be recorded for a given experimental tier. In order to evaluate the extent of community interest, an extensive outreach effort was conducted to present the proposed metadata specifications to members of several core-facilities and international bioimaging initiatives including the European Light Microscopy Initiative (ELMI), Global BioImaging (GBI), and European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute (EBI). Consequently, close ties were established between our endeavour and the undertakings of the recently established QUAlity Assessment and REProducibility for Instruments and Images in Light Microscopy global community initiative (9). As a result this flexible 4DN-BINA-OME (NBO namespace) framework (7, 8) represents a turning point towards achieving community-driven Microscopy Metadata standards that will increase data fidelity, improve repeatability and reproducibility, ease future analysis and facilitate the verifiable comparison of different datasets, experimental setups, and assays, and it demonstrates the method for future extensions. Such universally accepted microscopy standards would serve a similar purpose as the Encode guidelines successfully adopted by the genomic community (10, 11). The intention of this proposal is therefore to encourage participation, critiques and contributions from the entire imaging community and all stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, scientific publishers, and funders.

bioinformatics↗

A Latent Variable Model for Evaluation of Disparate Ratings of Stem Cell Colonies by Two Experts

The visual inspection of pluripotent stem cell colonies by microscopy is widely used as a primary method to assess the quality of the preparations and degree of pluripotency. The lack of ground truth and the possible inconsistency of evaluations from multiple experts within and between stem cell laboratories are sources of uncertainty about the state of the cells, the reproducibility of preparations, and the efficiency of expansion protocols. To examine how to evaluate the level of confidence one has in disparate rating from experts, we explored a statistical method for assessing the differences in ratings of pluripotent stem cells by two different experts. Two experts rated phase contrast microscope images of human embryonic stem cell (hESC) colonies on a scale of 1 (poor) to 5 (maximum pluripotency character) but agreed with one another only 48% of the time. To assess whether experts used similar criteria to rate colonies, we developed custom image feature algorithms based on the stated visual criteria provided by the experts for selection of colonies. These features, plus others, were then used to develop pluripotency scoring algorithms trained to reflect ratings of both experts. We treated expert ratings as inexact indicators of a continuous pluripotency score and considered the inconsistency between expert ratings in developing our models. The model suggests that the two experts use somewhat different scales for discriminating between colony quality. Covariance analysis indicated that both experts use features that are not included in the model. Two image features, colony perimeter and a feature based on texture, were the most important for both experts for predicting the ratings. Interestingly, colony perimeter was not one of the expert-provided criteria for rating colonies, showing that this modeling approach allowed identification of features that the experts were not aware they were using. A linear model based on both experts identified each experts top-rated colonies as well as, or better than, the ratings of the other expert, as indicated by receiver operator characteristic curve analysis. By providing an understanding of the differences and similarities in disparate sets of expert ratings, this analysis helps to establish confidence in the ratings and the criteria for ratings, even when the experts disagree.

bioinformatics↗