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3D breast cancer tissue reconstruction

Author(s): Jeremy M Wojdak1, Kerri-Ann Norton2

1. Radford University 2. Johns Hopkins University

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This module allows students to follow a researcher as she discovers new features of breast cancer tissue architecture, using 3D reconstructions of histological specimens.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 2.0 - published on 17 Sep 2018 doi:10.25334/Q4WM6W - cite this


This module was created as a way to get students thinking in new quantitative ways using cutting edge breast cancer research as the context for discovery, letting students experience the utility of math in science first-hand. Students follow along with a simplified but realistic approximation of the original research by reconstructing tissues in 3D to test predictions from a mechanistic agent-based model. There are several quantitative "side trips" that instructors can elect to include/exclude, that expose students to mathematical ideas used to automate image analyses. No prior instructor experience in these topics is assumed or necessary.

Potential Learning Objectives:

  • Students will be able to use sums of squares to describe differences.
  • Students will use image analysis software to generate data from an image set.
  • Students will use image analysis software to generate a 3D data visualization from an image set.
  • Students will be able to construct and interpret frequency histograms.
  • Students will gain appreciation for the interplay between modeling and empirical work.
  • Students will gain appreciation for the utility of mathematics and computation for biology and medicine.
  • Students will write a discussion section in typical scientific literature format.


  • Students will apply programming concepts to automate a series of procedures.
  • Students will quantify aspects of 3D shapes using image analysis.


This version includes an updated image database. 

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