Treating Tet Spells With Disopyramide in a 72-Year-Old Awaiting Primary Repair of Tetralogy of Fallot.

An adult with unrepaired tetralogy of Fallot presented with frequent tet spells. Her course was complicated by severe cyanotic spells and tachycardia-bradycardia syndrome that limited beta blocker use to stabilize her spells. She markedly improved after disopyramide initiation and underwent successful tetralogy of Fallot repair with excellent functional outcome.

Transplant and mechanical circulatory support in patients with adult congenital heart disease.

Advances in surgery and pediatric care over the past decades have achieved improved survival for children born with congenital heart disease (CHD) and have produced a large, growing population of patients with adult congenital heart disease (ACHD). Heart failure has emerged as the leading cause of death and a major cause of morbidity among the ACHD population, while as little evidence supports the efficacy of guideline-directed medical therapies in this population. It is increasingly important that clinicians caring for these patients understand how to utilize mechanical circulatory support (MCS) in ACHD.

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

Introduction: The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated.

Connecting Markov random fields and active contour models: application to gland segmentation and classification.

We introduce a Markov random field (MRF)-driven region-based active contour model (MaRACel) for histological image segmentation. This Bayesian segmentation method combines a region-based active contour (RAC) with an MRF. State-of-the-art RAC models assume that every spatial location in the image is statistically independent, thereby ignoring valuable contextual information among spatial locations.

Image Analysis in Surgical Pathology.

Digitization of glass slides of surgical pathology samples facilitates a number of value-added capabilities beyond what a pathologist could previously do with a microscope. Image analysis is one of the most fundamental opportunities to leverage the advantages that digital pathology provides. The ability to quantify aspects of a digital image is an extraordinary opportunity to collect data with exquisite accuracy and reliability.

Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei.

Color nonstandardness--the propensity for similar objects to exhibit different color properties across images--poses a significant problem in the computerized analysis of histopathology. Though many papers propose means for improving color constancy, the vast majority assume image formation via reflective light instead of light transmission as in microscopy, and thus are inappropriate for histological analysis. Previously, we presented a novel Bayesian color segmentation algorithm for histological images that is highly robust to color nonstandardness; this algorithm employed the expectation maximization (EM) algorithm to dynamically estimate for each individual image the probability density functions that describe the colors of salient objects.

Class-specific weighting for Markov random field estimation: application to medical image segmentation.

Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity).

Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification.

Introduction: The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves.

Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models.

In this paper we present a system for detecting regions of carcinoma of the prostate (CaP) in H&E stained radical prostatectomy specimens using the color fractal dimension. Color textural information is known to be a valuable characteristic to distinguish CaP from benign tissue. In addition to color information, we know that cancer tends to form contiguous regions.

Image microarrays (IMA): Digital pathology's missing tool.

Introduction: The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository.

An active learning based classification strategy for the minority class problem: application to histopathology annotation.

Background: Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g.

Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm.

For personalization of medicine, increasingly clinical and demographic data are integrated into nomograms for prognostic use, while molecular biomarkers are being developed to add independent diagnostic, prognostic, or management information. In a number of cases in surgical pathology, morphometric quantitation is already performed manually or semi-quantitatively, with this effort contributing to diagnostic workup. Digital whole slide imaging, coupled with emerging image analysis algorithms, offers great promise as an adjunctive tool for the surgical pathologist in areas of screening, quality assurance, consistency, and quantitation.

Automated vector selection of SIVQ and parallel computing integration MATLAB™: Innovations supporting large-scale and high-throughput image analysis studies.

Introduction: Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector's sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count.

Weighted maximum posterior marginals for random fields using an ensemble of conditional densities from multiple Markov chain Monte Carlo simulations.

The ability of classification systems to adjust their performance (sensitivity/specificity) is essential for tasks in which certain errors are more significant than others. For example, mislabeling cancerous lesions as benign is typically more detrimental than mislabeling benign lesions as cancerous. Unfortunately, methods for modifying the performance of Markov random field (MRF) based classifiers are noticeably absent from the literature, and thus most such systems restrict their performance to a single, static operating point (a paired sensitivity/specificity).

Markov random field driven region-based active contour model (MaRACel): application to medical image segmentation.

In this paper we present a Markov random field (MRF) driven region-based active contour model (MaRACel) for medical image segmentation. State-of-the-art region-based active contour (RAC) models assume that every spatial location in the image is statistically independent of the others, thereby ignoring valuable contextual information. To address this shortcoming we incorporate a MRF prior into the AC model, further generalizing Chan & Vese's (CV) and Rousson and Deriche's (RD) AC models.

Development of a stochastic model for the efficacy of NRTIs using known mechanisms of action.

We analyze the mechanisms by which nucleoside-analogue reverse transcriptase inhibitors, the most common class of drugs used in the treatment of HIV-1, exert their antiviral effects. We then seek to identify ways in which those known mechanisms can be employed to generate mathematical models for drug efficacy in terms of measurable physical values. We demonstrate that the probability a NRTI instead of a natural nucleotide is included can be expressed in terms of intracellular drug concentrations, natural nucleotide concentrations, and relevant rate constants derived from reverse transcriptase's mechanism of nucleotide addition.

High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions.

Integrated diagnostics: a conceptual framework with examples.

With the advent of digital pathology, imaging scientists have begun to develop computerized image analysis algorithms for making diagnostic (disease presence), prognostic (outcome prediction), and theragnostic (choice of therapy) predictions from high resolution images of digitized histopathology. One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer vision and image processing algorithms. Over the last decade, manifold learning and non-linear dimensionality reduction schemes have emerged as popular and powerful machine learning tools for pattern recognition problems.

Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology.

The identification of phenotypic changes in breast cancer (BC) histopathology on account of corresponding molecular changes is of significant clinical importance in predicting disease outcome. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with nodal metastasis and distant recurrence in HER2+ BC patients. In this paper, we present a computer-aided diagnosis (CADx) scheme to automatically detect and grade the extent of LI in digitized HER2+ BC histopathology.

Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging.

With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of analysis can identify biologically distinct disease subclasses of breast cancer with distinct molecular signatures, differential recurrence rates and potentially, very different response to therapy.

Nonlinearities in stereoscopic phase-differencing.

Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, these phase-differencing techniques suffer a significant impediment: phase nonlinearities. In regions of phase nonlinearity, the signals under consideration possess properties that invalidate the use of phase for disparity estimation.