Fully Automated Lung Volume Assessment from MRI in a Population-based Child Cohort Study

被引：1

作者：

Ivanovska, Tatyana ^{[1
]}

Ciet, Pierluigi ^{[2
]}

Rerez-Rovira, Adria ^{[2
]}

Anh Nguyen ^{[2
]}

Tiddens, Harm ^{[2
]}

Duijts, Liesbeth ^{[2
]}

de Bruijne, Marleen ^{[2
]}

Woergoetter, Florentin ^{[1
]}

机构：

[1] Georg August Univ, Dept Computat Neurosci, Gottingen, Germany

[2] Univ Med Ctr Rotterdam, Erasmus MC, Rotterdam, Netherlands

来源：

PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 6 | 2017年

关键词：

MRI; Lung; Segmentation and Volumetry; Child Cohort; SEGMENTATION;

D O I：

10.5220/0006075300530058

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, a framework for fully automated lung extraction from magnetic resonance imaging (MRI) inspiratory data that have been acquired within a on-going epidemiological child cohort study is presented. The method's main steps are intensity inhomogeneity correction, denoising, clustering, airway extraction and lung region refinement. The presented approach produces highly accurate results (Dice coefficients >= 95%), when compared to semi-automatically obtained masks, and has potential to be applied to the whole study data.

引用

页码：53 / 58

页数：6

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