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Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.

Author information

1
Computer Science and Engineering, University of Washington, Seattle, WA.
2
Seattle Children's Research Institute, Seattle, WA.
3
Advanced Digital Sciences Center, Singapore.

Abstract

Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses. We compare several different sparse feature selection techniques: L1 regularized logistic regression, fused lasso, and clustering lasso (cLasso). We evaluate our methodology in three ways: 1) for classification of normal vs. abnormal skulls, 2) for comparing pre-operative to post-operative skulls, and 3) for retrieving skulls in order of abnormality severity as compared with the ordering of a craniofacial expert.

KEYWORDS:

L1 penalized logistic regression; clustering lasso (cLasso); cranial image (CI); craniosynostosis; fused lasso; sparse logistic regression model

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