基于参数统计形状建模和随机森林回归投票的拇指梯形-掌骨关节自动分割。

Pub Date : 2019-01-01 Epub Date: 2018-07-26 DOI:10.1080/21681163.2018.1501765
Marco T Y Schneider, Ju Zhang, Joseph J Crisco, Arnold-Peter C Weiss, Amy L Ladd, Poul M F Nielsen, Thor Besier
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引用次数: 3

摘要

我们提出了一种自动流水线,用于从临床CT图像中创建形状建模合适的梯形掌骨(TMC)关节参数网格,用于批量处理和分析。该方法采用三维随机森林回归投票(RFRV)和统计形状模型(SSM)分割。该方法在65张CT图像的验证实验中得到验证,随机抽取其中15张从训练集中排除进行测试。初步结果显示,第一掌骨和斜骨的平均均方根误差分别为1.066 mm和0.632 mm,每张CT图像的分割时间约为2分钟,有望为批量处理提供准确的TMC关节骨三维网格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.

Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.

Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.

Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.

We propose an automatic pipeline for creating shape modelling suitable parametric meshes of the trapeziometacarpal (TMC) joint from clinical CT images for the purpose of batch processing and analysis. The method uses 3D random forest regression voting (RFRV) with statistical shape model (SSM) segmentation. The method was demonstrated in a validation experiment involving 65 CT images, 15 of which were randomly selected to be excluded from the training set for testing. With mean root mean squared (RMS) errors of 1.066 mm and 0.632 mm for the first metacarpal and trapezial bones respectively, and a segmentation time of ~2 minutes per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.

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