Two Methods for Validating Brain Tissue Classifiers

M. Martin-Fernandez, S. Bouix, L. Ungar, R. W. McCarley, M. E. Shenton
Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'05)
Pages 515-522
October, 2005

Download full paper

Abstract

In this paper, we present an evaluation of seven automatic brain tissue classifiers based on level of agreements. A number of agreement measures are explained, and we show how they can be used to compare different segmentation techniques. We use the Simultaneous Truth and Performance Level Estimation (STAPLE) of Warfield et al. but also introduce a novel evaluation technique based on the Williams' index. The methods are evaluated using these two techniques on a population of forty subjects, each having an SPGR scan and a co-registered T2 weighted scan. We provide an interpretation of the results and show how similar the output of the STAPLE analysis and Williams' index are. When no ground truth is required, we recommend the use of Williams' index as it is easy and fast to compute.

Reference

Martin-Fernandez M, Bouix S, Ungar L, McCarley RW, Shenton ME. Two methods for validating brain tissue classifiers. In Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'05), Lecture Notes in Computer Science 3749. Palm Springs, CA, USA, 2005;515-522.

Bibtex entry

@InProceedings{martin-fernandezMICCAI05,
  author         = {M. Martin-Fernandez and S. Bouix and L. Ungar and R. W.    
                   McCarley  and M. E. Shenton},                               
  title          = {Two Methods for Validating Brain Tissue Classifiers},      
  booktitle      = {Eighth International Conference on Medical Image Computing 
                   and  Computer-Assisted Intervention (MICCAI'05)},           
  pages          = {515--522},                                                 
  year           = {2005},                                                     
  series         = {Lecture Notes in Computer Science 3749},                   
  address        = {Palm Springs, CA, USA},                                    
  month          = {October}
}

Grants

NIH K02-MH01110, NIH R01-MH50747, NIH U54-EB005149 (NAMIC), NIH R01-MH40799, Fulbright FU2003-0968