title: Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.
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:L1regularized 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.
authors.affiliation: Computer Science and Engineering, University of Washington, Seattle, WA.
authors.forename: Shulin
authors.initials: S
authors.lastname: Yang
authors.affiliation: Seattle Children's Research Institute, Seattle, WA.
authors.forename: Linda
authors.initials: L
authors.lastname: Shapiro
authors.affiliation: Advanced Digital Sciences Center, Singapore.
authors.forename: Michael
authors.initials: M
authors.lastname: Cunningham
authors.forename: Matthew
authors.initials: M
authors.lastname: Speltz
authors.forename: Craig
authors.initials: C
authors.lastname: Birgfeld
authors.forename: Indriyati
authors.initials: I
authors.lastname: Atmosukarto
authors.forename: Su-In
authors.initials: SI
authors.lastname: Lee
keywords: L1 penalized logistic regression; clustering lasso (cLasso); cranial image (CI); craniosynostosis; fused lasso; sparse logistic regression model