Abstract
This paper presents a framework for unsupervised learning of shape complexity in the developing brain. It learns the complexity in different brain structures by applying several shape complexity measures to each individual structure, and then using feature selection to select the measures that best describe the changes in complexity of each structure. Then, feature selection is applied again to assign a score to each structure, in order to find which structure can be a good biomarker of brain development. This study was carried out using T2-weighted MR images from 224 premature neonates (the age range at the time of scan was 26.7 to 44.86 weeks post-menstrual age). The advantage of the proposed framework is that one can extract as many ROIs as desired, and the framework automatically finds the ones which can be used as good biomarkers. However, the example application focuses on neonatal brain image data, the proposed framework for combining information from multiple measures may be applied more generally to other populations and other forms of imaging data.
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Serag, A. et al. (2012). Unsupervised Learning of Shape Complexity: Application to Brain Development. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2012. Lecture Notes in Computer Science, vol 7570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33555-6_8
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DOI: https://doi.org/10.1007/978-3-642-33555-6_8
Publisher Name: Springer, Berlin, Heidelberg
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