This module allows students to follow a researcher as she discovers new features of breast cancer tissue architecture, using 3D reconstructions of histological specimens.
Listed in Teaching Materials | resource by group AIMS: Analyzing Images to learn Mathematics and Statistics
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.
Cite this work
Researchers should cite this work as follows:
- Jeremy M Wojdak, Kerri-Ann Norton (2018). 3D breast cancer tissue reconstruction. AIMS: Analyzing Images to learn Mathematics and Statistics, (Version 2.0). QUBES Educational Resources. doi:10.25334/Q4WM6W
This version includes an updated image database.
AIMS: Analyzing Images to learn Mathematics and Statistics
This publication belongs to the AIMS: Analyzing Images to learn Mathematics and Statistics group.
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