Multi-structure segmentation of multi-modal brain images using artificial neural networks

Eun Young Kim; Hans Johnson

doi:10.1117/12.844613

12 March 2010 Multi-structure segmentation of multi-modal brain images using artificial neural networks

Eun Young Kim, Hans Johnson

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76234B (2010) https://doi.org/10.1117/12.844613
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

A method for simultaneous segmentation of multiple anatomical brain structures from multi-modal MR images has been developed. An artificial neural network (ANN) was trained from a set of feature vectors created by a combination of high-resolution registration methods, atlas based spatial probability distributions, and a training set of 16 expert traced data sets. A set of feature vectors were adapted to increase performance of ANN segmentation; 1) a modified spatial location for structural symmetry of human brain, 2) neighbors along the priors descent for directional consistency, and 3) candidate vectors based on the priors for the segmentation of multiple structures. The trained neural network was then applied to 8 data sets, and the results were compared with expertly traced structures for validation purposes. Comparing several reliability metrics, including a relative overlap, similarity index, and intraclass correlation of the ANN generated segmentations to a manual trace are similar or higher to those measures previously developed methods. The ANN provides a level of consistency between subjects and time efficiency comparing human labor that allows it to be used for very large studies.

Citation Download Citation

Eun Young Kim and Hans Johnson "Multi-structure segmentation of multi-modal brain images using artificial neural networks", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234B (12 March 2010); https://doi.org/10.1117/12.844613

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