Welcome to the Center for Bioimage Informatics!

Events Calendar

6th Annual Bioimaging Day

The Center for Bioimage Informatics hosts its 6th annual Bioimaging Day on February 18th, 2010. The goal of the day is to bring together faculty and researchers from the Pittsburgh area to hear about biological and medical problems in which images are the primary data source, computational and statistical methods for automated analysis of images, and current and planned infrastructure to facilitate acquisition, management and interpretation of biomedical images.

• Announcement.
• Program.
• Directions. Singleton Room, Roberts Engineering Hall, CMU campus (Roberts Engineering Hall is building 21 on the campus map).
• Previous Bioimaging Days.

2010 Bioimage Informatics Conference

We are slated to host the next Bioimage Informatics Conference, in September of 2010. See the conference page for more details.

2009 Bioimage Informatics Conference

CBI Team was well represented at the latest Bioimage Informatics Conference held at Janelia Farm Research Center in April 2009. From left to right: Ge Yang, Gustavo Rohde, Jelena Kovacevic, Bob Murphy, Sam Chen (alumnus) and Ting Zhao (alumnus).

CBI Monthly Lunch Seminar Series

All CBI seminars take place at 12pm in the CBI Conference Room, Hamerschlag Hall C119. Contact CBI seminar host Gustavo Rohde for more information.

May 7, 2009, 11am (note time change)

Ann B. Lee

Department of Statistics, Carnegie Mellon University

Diffusion maps with an application to texture discrimination

For naturally occurring data, the dimension of the given input space is often very large while the data themselves have a low intrinsic dimensionality. Diffusion map is a non-linear spectral method for transforming data into a coordinate system that efficiently reveal the geometric structure -- in particular, the "connectivity" -- of the data. In this talk, I will first review the basic ideas behind the diffusion framework and then discuss my current work on texture discrimination by a novel geometry-based metric on distributions. (Part of this work is joint with C. Schafer

February 26, 2009

Zoltan N. Oltvai

Department of Pathology, University of Pittsburgh

Digital Pathology and computational disease modeling

Advances in genomics, imaging, and systems biology promises to contribute significantly to the diagnostic process in pathology. In this talk I will review two of our recent studies that aim to contribute toward this goal.

January 29, 2009

Justing Newberg

Center for Bioimage Informatics, Carnegie Mellon University

Automated Analysis of Subcellular Protein Patterns in Human Tissues

Systematic information on the subcellular distributions of proteins is required for more accurate cell models that can be applied to clinically relevant cases; moreover, such information plays an increasingly important role in medical diagnoses. Given the number of proteins, conditions, and cell and tissue types for which information is needed, there is a critical need for automated, high throughput acquisition and analysis of subcellular location patterns. Automated pattern recognition methods have been shown to be effective at determining protein patterns in limited cell culture datasets. We have adapted these machine learning methods to analyze protein patterns in tissue images. For this purpose, we have used the extensive collection of images in the Human Protein Atlas, which contains over 6000 proteins in immunohistochemically stained tissues. Our initial work on a subset of the Atlas showed that we can determine protein locations across 45 different tissue types with a high degree of accuracy. In this talk, I will discuss various methods- such as classification, clustering, and segmentation- for scaling automated analysis to a larger set of proteins in the Atlas, and I will show preliminary results obtained using these methods.

News and Events

Amina Chebira had a paper accepted for publication in the Journal of Applied and Harmonic Computational Analysis, “Classifying Convex Sets with Frames” (joint work with Matt Fickus and Jelena Kovacevic). In this work, Amina is the first to put forth a framework towards answering the question of why frames (redundant signal representations) perform better than their nonredundant counterpart in real-world classification problems. She is now a postdoctoral researcher in the group of Prof. Martin Vetterli, at EPFL, Lausanne, Switzerland.

CBI was well represented at the last BMES conference:

• Ryan Kellogg, Daniel Delubac, Amina Chebira, Jonathan S. Minden, Jelena Kovacevic, and Stefan F. Zappe, “Imaging technologies for high-throughput Drosophila functional genomics screens”.

• Chris Highley, Sasha Bakhru, Stefan Zappe, “Hyaluronic acid derivatives or complex coacervation and cellular encapsulation”.

• Usha Kuppuswamy, Sasha Bakhru, Daniel Delubac, Stefan Zappe, “Perfusion microbioreactor for human adult neural stem cell expansion”.