Browsing by Subject "Pattern Recognition, Automated"

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    Automated quality control in nuclear medicine using the structured noise index.
    (Journal of applied clinical medical physics, 2020-04) Nelson, Jeffrey S; Samei, Ehsan

    Purpose

    Daily flood-field uniformity evaluation serves as the central element of nuclear medicine (NM) quality control (QC) programs. Uniformity images are traditionally analyzed using pixel value-based metrics, that is, integral uniformity (IU), which often fail to capture subtle structure and patterns caused by changes in gamma camera performance, requiring visual inspections which are subjective and time demanding. The goal of this project was to implement an advanced QC metrology for NM to effectively identify nonuniformity issues, and report issues in a timely manner for efficient correction prior to clinical use. The project involved the implementation of the program over a 2-year period at a multisite major medical institution.

    Methods

    Using a previously developed quantitative uniformity analysis metric, the structured noise index (SNI) [Nelson et al. (2014), \textit{J Nucl Med.}, \textbf{55}:169-174], an automated QC process was developed to analyze, archive, and report on daily NM QC uniformity images. Clinical implementation of the newly developed program ran in parallel with the manufacturer's reported IU-based QC program. The effectiveness of the SNI program was evaluated over a 21-month period using sensitivity and coefficient of variation statistics.

    Results

    A total of 7365 uniformity QC images were analyzed. Lower level SNI alerts were generated in 12.5% of images and upper level alerts in 1.7%. Intervention due to image quality issues occurred on 26 instances; the SNI metric identified 24, while the IU metric identified eight. The SNI metric reported five upper level alerts where no clinical engineering intervention was deemed necessary.

    Conclusion

    An SNI-based QC program provides a robust quantification of the performance of gamma camera uniformity. It operates seamlessly across a fleet of multiple camera models and, additionally, provides effective workflow among the clinical staff. The reliability of this process could eliminate the need for visual inspection of each image, saving valuable time, while enabling quantitative analysis of inter- and intrasystem performance.
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    Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
    (PLoS Comput Biol, 2011-07) Pruteanu-Malinici, Iulian; Mace, Daniel L; Ohler, Uwe
    Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.
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    Gene selection using iterative feature elimination random forests for survival outcomes.
    (IEEE/ACM Trans Comput Biol Bioinform, 2012-09) Pang, Herbert; George, Stephen L; Hui, Ken; Tong, Tiejun
    Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.
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    Instrument independent diffuse reflectance spectroscopy.
    (J Biomed Opt, 2011-01) Yu, Bing; Fu, Henry L; Ramanujam, Nirmala
    Diffuse reflectance spectroscopy with a fiber optic probe is a powerful tool for quantitative tissue characterization and disease diagnosis. Significant systematic errors can arise in the measured reflectance spectra and thus in the derived tissue physiological and morphological parameters due to real-time instrument fluctuations. We demonstrate a novel fiber optic probe with real-time, self-calibration capability that can be used for UV-visible diffuse reflectance spectroscopy in biological tissue in clinical settings. The probe is tested in a number of synthetic liquid phantoms over a wide range of tissue optical properties for significant variations in source intensity fluctuations caused by instrument warm up and day-to-day drift. While the accuracy for extraction of absorber concentrations is comparable to that achieved with the traditional calibration (with a reflectance standard), the accuracy for extraction of reduced scattering coefficients is significantly improved with the self-calibration probe compared to traditional calibration. This technology could be used to achieve instrument-independent diffuse reflectance spectroscopy in vivo and obviate the need for instrument warm up and post∕premeasurement calibration, thus saving up to an hour of precious clinical time.