Fully automated lung volume assessment from MRI in a population-based child cohort study

Tatyana Ivanovska, Pierluigi Ciet, Adria Rerez-Rovira, Anh Nguyen, Harm Tiddens, Liesbeth Duijts, Marleen De Bruijne, Florentin Wörgötter

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationVISAPP
EditorsJose Braz, Alain Tremeau, Francisco Imai
Pages53-58
Number of pages6
ISBN (Electronic)9789897582271
Publication statusPublished - 2017
Event12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 - Porto, Portugal
Duration: 27 Feb 20171 Mar 2017

Publication series

SeriesVISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume6

Conference

Conference12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Country/TerritoryPortugal
CityPorto
Period27/02/171/03/17

Bibliographical note

Publisher Copyright: © 2017 by SCITEPRESS - Science and Technology Publications, Lda.

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