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IEEE International Symposium on biomedical Imaging

作者： Hao-Chih Lee Ge Yang Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Department of Biomedical Engineering Lane Center for Computational Bialogy

ISBN: (纸本)9781467393300

Intracellular transport is the process of distributing and collecting materials, often packaged in different forms of cargoes, to fulfill changing structural and functional needs within the cell. Although the molecular machinery for intracellular transport has largely been identified, how this process is regulated globally in space and time remains unclear. In a step towards answering this question, we developed an image-based computational method for characterizing spatiotemporal dynamics of intracellular transport at the whole-cell scale to separate signals of moving and stationary cargoes. Specifically, we extended an algorithm that we developed previously for removing background fluorescence of static images. This separation allows us to extract from time-lapse images the global pattern of cargo movement without single particle tracking of individual cargoes. Based on this separation, we characterized the spatiotemporal dynamics of intracellular transport in terms of the spatial localization of stationary cargoes and the spatiotemporal patterns of cargo movement, respectively. We first tested and validated our methods using synthetic images. We then used our methods to analyze time-lapse images of Lamp1 transport and found different global patterns of transport behavior.

关键词： Spatiotemporal phenomena Signal resolution Signal to noise ratio Optimization Particle tracking Proteins Space stations

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A symmetric deformation-based similarity measure for shape analysis

A symmetric deformation-based similarity measure for shape a...

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IEEE International Symposium on biomedical Imaging

作者： S. Kolouri D. Slepcev G. K. Rohde Center for Bioimage Informatics Biomedical Engineering Department Carnegie Mellon University Pittsburgh PA US Lane Center for Computational Biology Carnegie Mellon University Pittsburgh PA

Statistical modeling of cellular/subcellular shapes is required for quantifying shape variations and understanding complex cell behaviors. Such statistical models rely on the choice of a proper similarity measure. In this paper we introduce a symmetric deformation-based similarity measure for cellular shape analysis. The proposed method is based on finding an optimal diffeomorphic mapping between shape images that minimizes a physically meaningful energy function. We compare our proposed method to the large deformation dif-feomorphic metric mapping (LDDMM) and the standard Euclidean metric on a nuclei dataset and show that the proposed method outperforms the others in capturing the variations in the dataset.

关键词： Shape Shape measurement biomedical measurement Optimization Euclidean distance Energy measurement

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Learning and visualizing statistical relationships between protein distributions from microscopy images

Learning and visualizing statistical relationships between p...

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IEEE International Symposium on biomedical Imaging

作者： Soheil Kolouri Saurav Basu Gustavo K. Rohde IBM India Research Laboratory New Dehli India Department of Biomedical Engineering Center for Bioimage Informatics Lane Center for Computational Biology Carnegie Mellon University Pittsburgh PA Electrical and Computer Engineering

Multichannel microscopy has emerged as a technique for imaging multiple targets (molecules, protein distributions, etc.) simultaneously. Discovering the relative changes in these targets (i.e. distribution of different proteins) is fundamental for understanding cell structure and function. We describe a new method for quantifying and visualizing relationships between multiple targets, from a set of segmented multichannel cells. The method is based on combining the canonical correlation analysis technique with a framework for analyzing images based on the concept of optimal mass transportation. We apply the method towards understanding chromatin distribution in cancer nuclei as a function of nuclear envelope shape. We also show that sub cellular distribution of mitochondria can be used to predict the sub cellular localization of actin fibers in yeast cells. Finally, we also describe the application of the method towards understanding relationships between nuclear and cellular shapes in 2D HeLa cells. We believe that the method could serve as a general tool for mining relationships between different sub cellular protein/molecule distributions as well as organelle shapes.

关键词： Proteins Correlation Shape Image segmentation Microscopy Protein engineering

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Damage quantification and localization algorithms for indirect SHM of bridges

Damage quantification and localization algorithms for indire...

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The 7th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2014)(第七届国际桥梁维护安全与管理会议)

作者： G.Lederman Z.Wang J.Bielak H.Noh J.H.Garrett S.Chen J.Kova(c)evi(c) F.Cerda P.Rizzo Department of Civil and Environmental Engineering Carnegie Mellon UniversityPittsburghPAUSA Department of Biomedical Engineering & Center for Bioimage Informatics & Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghPAUSA Departamento de Ingeniería Civil Universidad de ConcepciónConcepciónChile Department of Civil and Environmental EngineeringUniversity of PittsburghPAUSA

This paper presents algorithms for diagnosing the severity and location of damage in a laboratory bridge *** use signal processing and machine learning approaches to analyze the vibration responses collected both directly from the bridge model and indirectly from a vehicle passing over the *** work expands on previous work by Cerda et al.(2013).

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A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

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Journal of pathology informatics 2014年第1期5卷 19页

作者： Ramamurthy Bhagavatula Michael T McCann Matthew Fickus Carlos A Castro John A Ozolek Jelena Kovacevic Massachusetts Institute of Technology Lincoln Laboratory Boston MA USA. Department of Biomedical Engineering Center for Bioimage Informatics Pittsburgh USA. Department of Mathematics and Statistics Air Force Institute of Technology Wright-Patterson Air Force Base OH USA. Department of Obstetrics and Gynecology Magee-Womens Research Institute and Foundation of the University of Pittsburgh Pittsburgh USA. Department of Pathology Children's Hospital of Pittsburgh of the University of Pittsburgh Pittsburgh PA USA. Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh USA

UNLABELLED:We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples. BACKGROUND:H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness. METHODS:We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density". These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm. RESULTS:Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach. CONCLUSIONS:The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.

关键词： Automated histology classification segmentation

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Nanometer resolution tracking and modeling of bidirectional axonal cargo transport

Nanometer resolution tracking and modeling of bidirectional ...

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IEEE International Symposium on biomedical Imaging

作者： Minhua Qiu Hao-Chih Lee Ge Yang Department of Biomedical Engineering Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Department of Biomedical Engineering Lane Center for Computational Biology Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781457718571

Survival and function of neurons depend critically on bidirectional transport of essential cargoes within their axons. To study related molecular mechanisms, we developed an automated single particle tracking method that combines nanometer resolution detection of cargo positions with kymograph-based multiple hypothesis tracking of cargo motion for precise and complete recovery of cargo trajectories. Experiments confirmed that the method can precisely track vesicles in dense axonal traffic of Drosophila motor neurons at 10~20 nm resolution and reliably resolve their trajectories in events of crossing, merging, and splitting. Recovered cargo trajectories were statistically analyzed to identify motion patterns and computationally represented using hidden Markov models to characterize behavior changes. Together, these tools allowed us to reliably detect and measure subtle changes in dynamics of axonal transport and to discover previously unknown axonal transport properties.

关键词： Trajectory Hidden Markov models Nerve fibers Image resolution Tracking Computational modeling

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A template matching approach for segmenting microscopy images

A template matching approach for segmenting microscopy image...

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IEEE International Symposium on biomedical Imaging

作者： Cheng Chen Wei Wang John A. Ozolek Nuno Lages Steven J. Altschuler Lani F. Wu Gustavo K. Rohde Center for Bioimage Informatics Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Department of Pathology Children's Hospital of Pittsburgh Pittsburgh PA USA Department of Pharmacology University of Texas Southwestern Medical Center Dallas Dallas TX USA Center for Bioimage Informatics Department of Biomedical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

We describe a generic method for segmenting microscopy images based on supervised statistical modeling. The idea is to utilize example input segmentations to learn a statistical model of the shape and texture of the structures to be segmented. Segmentation of a test image then can be performed by maximizing the normalized cross correlation between the model and neighborhoods in the test image, followed by a final adjustment that utilizes nonrigid registration. We demonstrate the application of the method in segmenting several types of microscopy images of cells and nuclei.

关键词： Image segmentation Shape Microscopy Object segmentation biomedical imaging Computational modeling Principal component analysis

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Nanometer resolution imaging and tracking of axonal cargo transport in normal and degenerative neurons

Nanometer resolution imaging and tracking of axonal cargo tr...

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Asilomar Conference on Signals, Systems & Computers

作者： Ge Yang Department of Biomedical Engineering Lane Center for Computational Biology Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781467303217

Survival and function of neurons depend critically on bidirectional active transport of essential cargoes along axonal microtubules. Transport defects have been strongly implicated in many human neurodegenerative diseases such as Alzheimer's disease. To study related physiology and disease mechanisms, we developed methods to image and automatically track individual cargoes at nanometer resolution. Using these tools, we characterized and compared transport behavior in normal neurons and degenerative neurons in a Drosophila model of Alzheimer's disease. We found previously unknown properties of axonal transport, including that cargo movement in vivo in both anterograde and retrograde directions follows multiple velocity modes.

关键词： Trajectory Image resolution Nerve fibers Hidden Markov models Tracking Imaging

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COMPRESSION OF QRS COMPLEXES USING HERMITE EXPANSION

COMPRESSION OF QRS COMPLEXES USING HERMITE EXPANSION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Aliaksei Sandryhaila Jelena Kovacevic Markus Puschel Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Biomedical Engineering and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Computer Science ETH Zurich Zurich Switzerland

We propose an algorithm for the compression of ECG signals, in particular QRS complexes, based on the expansion of signals with compact support into a basis of discrete Hermite functions. These functions are obtained by sampling continuous Hermite functions, previously used for the compression of ECG signals. Our algorithm uses the theory of signal models based on orthogonal polynomials, and achieves higher compression ratios compared with previously reported algorithms, both those using Hermite functions, as well as those using the discrete Fourier and discrete cosine transforms.

关键词： Electrocardiography Polynomials Approximation algorithms Compression algorithms Transforms Approximation error

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A pixel classification system for segmenting biomedical images using intensity neighborhoods and dimension reduction

A pixel classification system for segmenting biomedical imag...

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IEEE International Symposium on biomedical Imaging

作者： Cheng Chen John A. Ozolek Wei Wang Gustavo K. Rohde Center for Bioimage Informatics Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Department of Pathology Children''s Hospital of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781424441273

We present an intensity neighborhood-based system for segmenting arbitrary biomedical image datasets using supervised learning. Because neighborhood methods are often associated with high-dimensional feature vectors, we explore a Principal Component Analysis (PCA) based method to reduce the dimensionality (and provide computational savings) of each neighborhood. Our results show that the system can accurately segment data in three applications: tissue segmentation from brain MR data, and histopathological images, and nuclei segmentation from fluorescence images. Our results also show that the dimension reduction method we described improves computational efficiency while maintaining similar accuracy.

关键词： Pixel Image segmentation Training Principal component analysis biomedical imaging Testing Support vector machines

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