Teaching Medical Image Analysis with the Insight Toolkit - Robotics Institute Carnegie Mellon University

Teaching Medical Image Analysis with the Insight Toolkit

Damion Michael Shelton, George D. Stetten, S. Aylward, L. Ibanez, Constantine Aaron Cois, and C. Stuart
Journal Article, Medical Image Analysis: Special Issue ITK Open science - combining open data and open source software: Medical image analysis with the Insight Toolkit, Vol. 9, No. 6, pp. 605 - 611, December, 2005

Abstract

We present several case studies which examine the role that the Insight Toolkit (ITK) played in three medical image analysis courses and several conference tutorials. These courses represent the first use of ITK in a teaching environment, and we believe that a discussion of the teaching approach in each case and the benefits and challenges of ITK will be useful to future medical image analysis course development. ITK was found to provide significant value in a classroom setting since it provides both working “canned” algorithms, including some recently developed methods that are unavailable elsewhere, as well as a framework for developing new techniques and applications. Several areas of difficulty, particularly in regards to code complexity and advanced object-oriented design techniques, have been identified which may make the learning curve of ITK somewhat more complex than a language such as Matlab™.

BibTeX

@article{Shelton-2005-9359,
author = {Damion Michael Shelton and George D. Stetten and S. Aylward and L. Ibanez and Constantine Aaron Cois and C. Stuart},
title = {Teaching Medical Image Analysis with the Insight Toolkit},
journal = {Medical Image Analysis: Special Issue ITK Open science - combining open data and open source software: Medical image analysis with the Insight Toolkit},
year = {2005},
month = {December},
volume = {9},
number = {6},
pages = {605 - 611},
}