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.

Additional Metadata
Keywords Child Cohort, Lung, MRI, Segmentation and Volumetry
Persistent URL dx.doi.org/10.5220/0006075300530058, hdl.handle.net/1765/107339
Conference 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Citation
Ivanovska, T. (Tatyana), Ciet, P, Rerez-Rovira, A. (Adria), Nguyen, A. (Anh), Tiddens, H.A.W.M, Duijts, L, … Wörgötter, F. (Florentin). (2017). Fully automated lung volume assessment from MRI in a population-based child cohort study. In VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 53–58). doi:10.5220/0006075300530058