Luis Pedro Coelho

Publications

• 2.40

  Impact points

  Automated image analysis for high-content screening and analysis.

  Aabid Shariff, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn, Robert F Murphy

  Journal of biomolecular screening. 08/2010; 15(7):726-34.

  The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell cul... [more] The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.
• 4.93

  Impact points

  Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing.

  Luis Pedro Coelho, Tao Peng, Robert F Murphy

  Bioinformatics (Oxford, England). 06/2010; 26(12):i7-12.

  Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of ... [more] Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them. We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location. We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy. http://murphylab.web.cmu.edu/software.

• Structured literature image finder: Parsing text and figures in biomedical literature.

  Amr Ahmed, Andrew Arnold, Luís Pedro Coelho, Joshua Kangas, Abdul-Saboor Sheikh, Eric P. Xing, William W. Cohen, Robert F. Murphy

  J. Web Sem. 01/2010; 8:151-154.

• Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature.

  Luís Pedro Coelho, Amr Ahmed, Andrew Arnold, Joshua Kangas, Abdul-Saboor Sheikh, Eric P Xing, William W Cohen, Robert F Murphy

  Lecture notes in computer science. 01/2010; 6004:23-32.

  SLIF uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. SLIF provides a publicly available searchable database... [more] SLIF uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. SLIF provides a publicly available searchable database (http://slif.cbi.cmu.edu).SLIF originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label.We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.

• Cell cycle dependence of protein subcellular location inferred from static, asynchronous images.

  Taraz E Buck, Arvind Rao, Luis Pedro Coelho, Margaret H Fuhrman, Jonathan W Jarvik, Peter B Berget, Robert F Murphy

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2009; 1:1016-9.

  Protein subcellular location is one of the most important determinants of protein function during cellular processes. Changes in protein behavior during the cell cycle are expected to be involved in cellular reprogramming during disease and development, and there is therefore a critical need to unde... [more] Protein subcellular location is one of the most important determinants of protein function during cellular processes. Changes in protein behavior during the cell cycle are expected to be involved in cellular reprogramming during disease and development, and there is therefore a critical need to understand cell-cycle dependent variation in protein localization which may be related to aberrant pathway activity. With this goal, it would be useful to have an automated method that can be applied on a proteomic scale to identify candidate proteins showing cell-cycle dependent variation of location. Fluorescence microscopy, and especially automated, high-throughput microscopy, can provide images for tens of thousands of fluorescently-tagged proteins for this purpose. Previous work on analysis of cell cycle variation has traditionally relied on obtaining time-series images over an entire cell cycle; these methods are not applicable to the single time point images that are much easier to obtain on a large scale. Hence a method that can infer cell cycle-dependence of proteins from asynchronous, static cell images would be preferable. In this work, we demonstrate such a method that can associate protein pattern variation in static images with cell cycle progression. We additionally show that a one-dimensional parameterization of cell cycle progression and protein feature pattern is sufficient to infer association between localization and cell cycle.

• NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS.

  Luís Pedro Coelho, Aabid Shariff, Robert F Murphy

  Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging. 01/2009; 5193098:518-521.

  Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images... [more] Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible.The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.

• Identifying Subcellular Locations from Images of Unknown Resolution.

  Luís Pedro Coelho, Robert F. Murphy

  Bioinformatics Research and Development, Second International Conference, BIRD 2008, Vienna, Austria, July 7-9, 2008, Proceedings; 01/2008

• Structured literature image finder: Parsing text and figures in biomedical literature

  Amr Ahmed, Andrew Arnold, Luis Pedro Coelho, Joshua Kangas, Abdul-Saboor Sheikh, Eric Xing, William Cohen, Robert F. Murphy

  Web Semantics: Science, Services and Agents on the World Wide Web.

  The SLIF project combines text-mining and image processing to extract structured information from biomedical literature.SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the image... [more] The SLIF project combines text-mining and image processing to extract structured information from biomedical literature.SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy images are further processed and classified according to the depicted subcellular localization.The results of this process can be queried online using either a user-friendly web-interface or an XML-based web-service. As an alternative to the targeted query paradigm, SLIF also supports browsing the collection based on latent topic models which are derived from both the annotated text and the image data.The SLIF web application, as well as labeled datasets used for training system components, is publicly available at http://slif.cbi.cmu.edu.