基于三维网格数据学习对未修复唇裂鼻畸形的严重程度进行分级
Learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh Data.
作者信息
Wu Jia, Tse Raymond, Shapiro Linda G
机构信息
Department of Electrical Engineering, University of Washington, Seattle, Washington, 98195, U.S.A.
Seattle Children's Hospital, Department of Surgery, University of Washington, Seattle, WA, 98195, U.S.A.
出版信息
Proc IAPR Int Conf Pattern Recogn. 2014 Aug;2014:460-464. doi: 10.1109/ICPR.2014.88.
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.
唇裂是一种导致上唇和鼻子畸形的出生缺陷。其严重程度差异很大，治疗结果受初始畸形的影响。对严重程度进行客观评估将有助于指导预后和治疗。然而，大多数评估都是主观的。本研究的目的是开发和测试基于计算机的唇裂严重程度定量测量方法。本文介绍了一种基于网格补丁的对称性测量方法，利用该方法计算机程序可以在人类婴儿面部的三维网格上对唇裂的严重程度进行排序。将三种基于计算机的定义面中部参考平面的方法与两种手动方法进行了比较。基于这些参考平面计算并评估了四种不同的对称性特征。结果表明，所提出的特征预测的排名与用作基准真值的专家提供的排名顺序高度相关。
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