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Proc IAPR Int Conf Pattern Recogn. 2014 Aug;2014:460-464.

Learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh Data.

Author information

1
Department of Electrical Engineering, University of Washington, Seattle, Washington, 98195, U.S.A.
2
Seattle Children's Hospital, Department of Surgery, University of Washington, Seattle, WA, 98195, U.S.A.
3
Department of Electrical Engineering, Computer Science and Engineering, University of Washington, Seattle, Washington, 98195, U.S.A.

Abstract

Cleft lip is a birth defect that results in deformity of the upper lip and nose. Its severity is widely variable and the results of treatment are influenced by the initial deformity. Objective assessment of severity would help to guide prognosis and treatment. However, most assessments are subjective. The purpose of this study is to develop and test quantitative computer-based methods of measuring cleft lip severity. In this paper, a grid-patch based measurement of symmetry is introduced, with which a computer program learns to rank the severity of cleft lip on 3D meshes of human infant faces. Three computer-based methods to define the midfacial reference plane were compared to two manual methods. Four different symmetry features were calculated based upon these reference planes, and evaluated. The result shows that the rankings predicted by the proposed features were highly correlated with the ranking orders provided by experts that were used as the ground truth.

KEYWORDS:

3D shape quantification; cleft lip; face symmetry; learning to rank

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