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骨质疏松症的统计形状和外观模型。

Statistical shape and appearance models in osteoporosis.

机构信息

Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Mechanical Engineering Department, The University of Sheffield, Sheffield, UK,

出版信息

Curr Osteoporos Rep. 2014 Jun;12(2):163-73. doi: 10.1007/s11914-014-0206-3.

Abstract

Statistical models (SMs) of shape (SSM) and appearance (SAM) have been acquiring popularity in medical image analysis since they were introduced in the early 1990s. They have been primarily used for segmentation, but they are also a powerful tool for 3D reconstruction and classification. All these tasks may be required in the osteoporosis domain, where fracture detection and risk estimation are key to reducing the mortality and/or morbidity of this bone disease. In this article, we review the different applications of SSMs and SAMs in the context of osteoporosis, and it concludes with a discussion of their advantages and disadvantages for this application.

摘要

自 20 世纪 90 年代初引入以来,形状统计模型(Statistical models (SMs) of shape (SSM))和外观统计模型(Statistical models (SMs) of appearance (SAM))在医学图像分析中越来越受欢迎。它们主要用于分割,但也是 3D 重建和分类的有力工具。在骨质疏松症领域,这些任务都可能是必需的,其中骨折检测和风险估计是降低这种骨骼疾病死亡率和/或发病率的关键。本文综述了 SSM 和 SAM 在骨质疏松症背景下的不同应用,并讨论了它们在该应用中的优缺点。

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