Semi-Automatic Teeth Segmentation in Cone-Beam Computed Tomography by Graph-Cut with Statistical Shape Priors

被引：0

作者：

Evain, Timothee ^{[1
,2
]}

Ripoche, Xavier ^{[2
]}

Atif, Jamal ^{[3
]}

Bloch, Isabelle ^{[1
]}

机构：

[1] Univ Paris Saclay, LTCI, Telecom ParisTech, Paris, France

[2] Carestream Dent, Croissy Beaubourg, France

[3] Univ Paris 09, PSL Res Univ, CNRS, UMR LAMSADE 7243, F-75016 Paris, France

来源：

2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017) | 2017年

关键词：

IMAGE SEGMENTATION; LEVEL-SET; MODEL;

D O I：

暂无

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images for which a reference segmentation was available, with an overall Dice coefficient above 0.95 and a global consistency error less than 0.005.

引用

页码：1197 / 1200

页数：4

共 12 条

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