CHUP1 restricts chloroplast movement and effector-triggered immunity in epidermal cells.

Chloroplast Unusual Positioning 1 (CHUP1) plays an important role in the chloroplast avoidance and accumulation responses in mesophyll cells. In epidermal cells, prior research showed silencing CHUP1-induced chloroplast stromules and amplified effector-triggered immunity (ETI); however, the underlying mechanisms remain largely unknown. CHUP1 has a dual function in anchoring chloroplasts and recruiting chloroplast-associated actin (cp-actin) filaments for blue light-induced movement.

Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping.

Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost. The increasing availability of satellite imagery and other remote sensing data has triggered research activities on development of algorithms for detection and monitoring of natural events. Here, we introduce an unsupervised subspace learning-based methodology that uses multi-temporal and multi-spectral satellite images to identify regions damaged by natural disasters.

QUANTIFYING ACTIN FILAMENTS IN MICROSCOPIC IMAGES USING KEYPOINT DETECTION TECHNIQUES AND A FAST MARCHING ALGORITHM.

The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental.

Feature Map Retargeting to Classify Biomedical Journal Figures.

In this work, we propose a layer to retarget feature maps in Convolutional Neural Networks (CNNs). Our "Retarget" layer densely samples values for each feature map channel at locations inferred by our proposed spatial attention regressor. Our layer increments an existing saliency-based distortion layer by replacing its convolutional components with depthwise convolutions.

A multiparametric magnetic resonance imaging-based virtual reality surgical navigation tool for robotic-assisted radical prostatectomy.

Objective: Increased computational power and improved visualization hardware have generated more opportunities for virtual reality (VR) applications in healthcare. In this study, we test the feasibility of a VR-assisted surgical navigation system for robotic-assisted radical prostatectomy.

Material And Methods: The prostate, all magnetic resonance imaging (MRI) visible tumors, and important anatomic structures like the neurovascular bundles, seminal vesicles, bladder, and rectum were contoured on a multiparametric MRI using an in-house segmentation software.

Intersection To Overpass: Instance Segmentation On Filamentous Structures With An Orientation-Aware Neural Network And Terminus Pairing Algorithm.

Filamentous structures play an important role in biological systems. Extracting individual filaments is fundamental for analyzing and quantifying related biological processes. However, segmenting filamentous structures at an instance level is hampered by their complex architecture, uniform appearance, and image quality.

A novel application of deep learning for single-lead ECG classification.

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG.

Stromule extension along microtubules coordinated with actin-mediated anchoring guides perinuclear chloroplast movement during innate immunity.

Dynamic tubular extensions from chloroplasts called stromules have recently been shown to connect with nuclei and function during innate immunity. We demonstrate that stromules extend along microtubules (MTs) and MT organization directly affects stromule dynamics since stabilization of MTs chemically or genetically increases stromule numbers and length. Although actin filaments (AFs) are not required for stromule extension, they provide anchor points for stromules.

Compound image segmentation of published biomedical figures.

Motivation: Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information.

Segmenting Compound Biomedical Figures into Their Constituent Panels.

Many of the figures in biomedical publications are compound figures consisting of multiple panels. Segmenting such figures into constituent panels is an essential first step for harvesting the visual information within the biomedical documents. Current figure separation methods are based primarily on gap-detection and suffer from over- and under-segmentation.

Semiautomated confocal imaging of fungal pathogenesis on plants: Microscopic analysis of macroscopic specimens.

The study of phenotypic variation in plant pathogenesis provides fundamental information about the nature of disease resistance. Cellular mechanisms that alter pathogenesis can be elucidated with confocal microscopy; however, systematic phenotyping platforms-from sample processing to image analysis-to investigate this do not exist. We have developed a platform for 3D phenotyping of cellular features underlying variation in disease development by fluorescence-specific resolution of host and pathogen interactions across time (4D).

Single-image vignetting correction from gradient distribution symmetries.

We present novel techniques for single-image vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the presumed optical center and passing through the pixel. An RG is an image gradient along the radial direction with respect to the optical center.

Contour extraction of Drosophila embryos.

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image.

Single-image vignetting correction.

In this paper, we propose a method for robustly determining the vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for vignetting estimation.

Estimation of ground-glass opacity measurement in CT lung images.

We propose to measure quantitatively the opacity property of each pixel in a ground-glass opacity tumor from CT images. Our method results in an opacity map in which each pixel takes opacity value of [0-1]. Given a CT image, our method accomplishes the estimation by constructing a graph Laplacian matrix and solving a linear equations system, with assistance from some manually drawn scribbles for which the opacity values are easy to determine manually.

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels.

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots.

De-enhancing the dynamic contrast-enhanced breast MRI for robust registration.

Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrast-enhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm.

Automatic contour tracking in ultrasound images.

In this paper, a new automatic contour tracking system, EdgeTrak, for the ultrasound image sequences of human tongue is presented. The images are produced by a head and transducer support system (HATS). The noise and unrelated high-contrast edges in ultrasound images make it very difficult to automatically detect the correct tongue surfaces.

Tongue motion averaging from contour sequences.

In this paper, a method to get the best representation of a speech motion from several repetitions is presented. Each repetition is a representation of the same speech captured at different times by sequence of ultrasound images and is composed of a set of 2D spatio-temporal contours. These 2D contours in different repetitions are time aligned first by a shape based Dynamic Programming (DP) method.