Validation of semiautomated segmentation algorithm with partial volume redistribution
John O. Glass, Wilburn E. Reddick, R. Grant Steen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Purpose - To reduce partial volume contamination, we present a linear interpolation combining quantitative T1 information with segmented base images. In addition, manual segmentation was completed for comparison to both of the techniques. Methods - To quantitatively assess T1, a precise and accurate inversion recovery (PAIR) sequence was acquired. The Kohonen SOM segmentation algorithm used the four base images as inputs and had nine output neurons. The segmented regions were manually classified by an expert for training a multi-layered backpropagation neural network to automate this process. A linear interpolation based on mean T1 relaxivity for each segmented class (regional method) and a pixel by pixel basis (pixel method) was performed. Manual segmentation was performed directly on base images by three observers. Differences between the techniques are reported as percent errors of the mean difference divided by the mean estimates of the manual segmentation. Results and Discussion -Within observer variances for the manual segmentation were less than 5.6% while between observer variances were 11.7% and 7.2% for white and gray matter respectively. The regional method had variances of 4.1% and 1.0% while the pixel method produced variances of 5.8% and 1.5% for white and gray matter, respectively, compared to the manual segmentation.