Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography.

Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms.

Deep learning classification of human colon tissues using spectroscopic OCT.

Screening programs for colorectal cancer (CRC) have had a profound impact on the morbidity and mortality of this disease by detecting and removing early cancers and precancerous adenomas with colonoscopy. However, CRC continues to be the third leading cause of cancer-related mortality in both men and woman, partly because of limitations in colonoscopy-based screening. Thus, novel strategies to improve the efficiency and effectiveness of screening colonoscopy are urgently needed.

Spectroscopic optical coherence tomography for classification of colorectal cancer in a mouse model.

Noninvasive diagnosis of the malignant potential of colon polyps can improve prevention of colorectal cancer without the need for time-consuming and expensive biopsies. This study examines the use of spectroscopic optical coherence tomography (OCT) to classify tissue from genetically engineered mouse models of early-stage adenoma (APC) and advanced adenocarcinoma (AKP) in which tumors are induced in the distal colon. The optical tissue properties of scattering power and scattering attenuation coefficient are evaluated by analyzing the imaging data collected from tissues.

Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.

We present a machine learning method for detecting and staging cervical dysplastic tissue using light scattering data based on a convolutional neural network (CNN) architecture. Depth-resolved angular scattering measurements from two clinical trials were used to generate independent training and validation sets as input of our model. We report 90.

A review of low-cost and portable optical coherence tomography.

Optical coherence tomography (OCT) is a powerful optical imaging technique capable of visualizing the internal structure of biological tissues at near cellular resolution. For years, OCT has been regarded as the standard of care in ophthalmology, acting as an invaluable tool for the assessment of retinal pathology. However, the costly nature of most current commercial OCT systems has limited its general accessibility, especially in low-resource environments.

A Flexible Antenna Array with Integrated Switching Matrix for Breast Cancer Detection.

In this report, we introduce a flexible board combining a custom switching circuit and 16 integrated antennas for a time-domain ultrawideband radar for breast health monitoring in one device. The goal of this study is to assess the suitability of the flexible prototype for tumor detection using carbon-polyurethane experimental breast models and comparing the performance to an earlier prototype with a rigid switching circuit and 16 separate antennas. The flexible antenna array allows direct contact with the patient skin while reducing the number of RF and DC cables needed in the previously reported system.

Prospective detection of cervical dysplasia with scanning angle-resolved low coherence interferometry.

We present a prospective clinical study using angle-resolved low-coherence interferometry (a/LCI) to detect cervical dysplasia via depth resolved nuclear morphology measurements. The study, performed at the Jacobi Medical Center, compares 80 a/LCI optical biopsies taken from 20 women with histopathological tissue diagnosis of co-registered physical biopsies. A novel instrument was used for this study that enables 2D scanning across the cervix without repositioning the probe.

Spatial scanning of a sample with two-dimensional angle-resolved low-coherence interferometry for analysis of anisotropic scatterers.

Angle-resolved low-coherence interferometry (a/LCI) measures depth-resolved angular scattering for cell nuclear morphology analysis. 2D a/LCI, developed to collect across two scattering planes, is currently limited by the lack of spatial scanning. Here we demonstrate 2D a/LCI scanning across a three-dimensional volume using an image rotation scheme and a scanning mirror.

Geometric quantification of features in large flow fields.

Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities.

Scalable multi-variate analytics of seismic and satellite-based observational data.

Over the past few years, large human populations around the world have been affected by an increase in significant seismic activities. For both conducting basic scientific research and for setting critical government policies, it is crucial to be able to explore and understand seismic and geographical information obtained through all scientific instruments. In this work, we present a visual analytics system that enables explorative visualization of seismic data together with satellite-based observational data, and introduce a suite of visual analytical tools.

Dynamic visualization of coexpression in systems genetics data.

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters.