A feature-based developmental model of the infant brain in structural MRI

Authors:
Matthew Toews;William M. Wells;Lilla Zöllei
Affiliations:
Harvard Medical School, Brigham and Women's Hospital;Harvard Medical School, Brigham and Women's Hospital;Harvard Medical School, Massachussetts General Hospital
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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Abstract

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.