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Development of an ITK Module for False Discovery Rate Analysis in Shape Analysis

This project aims to develop an algorithm and software module within the ITK framework to analyze the False Discovery Rate (FDR) in local shape assessments. Key tasks include converting existing non-ITK statistical methods, adding new FDR implementations, and conducting clinical comparisons based on male SPD caudate data. The project collaborators consist of experts from UNC, GE Research, and Harvard, and the results will be disseminated through the Insight Journal with an expected publication during the MICCAI open source workshop.

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Development of an ITK Module for False Discovery Rate Analysis in Shape Analysis

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  1. 2006 Summer PrWeek False Discovery Rate for Shape Analysis:Develop algorithm and ITK software module for False Discovery Rate (FDR) analysis. Plan/Expected Challenges/Publication Team Algorithms: False Discovery Rate(FDR) for local shape analysis. Martin Styner, UNC (algorithms) Jim Miller, GE Research (software support) Jim Levitt, VA Brockton, Harvard (clinical data) Software: Convert current non-ITK statistical analysis to ITK-compatible classes for current method. Add new implementation for FDR Clinical: Comparison to current results on male SPD caudate study. Expected Date for Insight Journal Publication: Submission to MICCAI open source workshop during project week or one week after that. Accomplished by end of Programming Week Raw • New Software classes for FDR • Testing on male SPD caudate data • Less conservative then permutation test based correction • Dissemination to BWH for other studies • Insight Journal publication MICCAI 2006 FDR PermTest M Styner, I Oguz, S Xu, C Brechbuhler, D Pantazis, J Levitt, M Shenton, G Gerig:Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM, Open Science Workshop at MICCAI 2006, Insight Journal http://hdl.handle.net/1926/215

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