LungMAP

Project Summary

Vivek Sriram (Computer Science and Math), Lina Yang (Biostatistics), and Pablo Ortiz (BME) spent ten weeks working in close collaboration with the Department of Biostatistics and Bioinformatics implementing an image analysis pipeline for immunofluorescence microscopy images of developing mouse lungs.

Themes and Categories
Year
2016

Project Results

Using the LungMAP image atlas (http://lungmap.net), the team developed an image segmentation pipeline to help researchers more effectively utilize open-access images of lungs in various developmental stages. The work of the Data+ team allows biologists and clinical researchers to quantify changes in lung structure during fetal development, and improve understanding of normal lung structure and function.

Download the Executive Summary (PDF)

Article on LungMAP project https://biostat.duke.edu/news/data-wraps

Faculty Sponsors

Project Manager

Participants

Disciplines Involved

  • Biostatistics
  • Biology
  • All quantitative STEM

Related People

Related Projects

Brooke Erikson (Economics/Computer Science), Alejandro Ortega (Math), and Jade Wu (Computer Science) spent ten weeks developing open-source tools for automatic document categorization, PDF table extraction, and data identification. Their motivating application was provided by Power for All’s Platform for Energy Access Knowledge, and they frequently collaborated with professionals from that organization.

Click here to read the Executive Summary

 

Jake Epstein (Statistics/Economics), Emre Kiziltug (Economics), and Alexander Rubin (Math/Computer Science) spent ten weeks investigating the existence of relative value opportunities in global corporate bond markets. They worked closely with a dataset provided by a leading asset management firm.

Click here for the Executive Summary

Maksym Kosachevskyy (Economics) and Jaehyun Yoo (Statistics/Economics) spent ten weeks understanding temporal patterns in the used construction machinery market and investigating the relationship between these patterns and macroeconomic trends.

They worked closely with a large dataset provided by MachineryTrader.com, and discussed their findings with analytics professionals from a leading asset management firm.

Click here to read the Executive Summary