3D human airway segmentation from high hesolution MR imaging

Fabien Baldacci; François Laurent; Patrick Berger; Gaël Dournes

doi:10.1117/12.2522948

15 March 2019 3D human airway segmentation from high hesolution MR imaging

Fabien Baldacci, François Laurent, Patrick Berger, Gaël Dournes

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110410Y (2019) https://doi.org/10.1117/12.2522948
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

In this article we present a method to extract the human airway tree from MR image. The assesment of morphometric data of bronchi is useful to estimate many diseases severity. Nowadays, the measurements are realized on CT scan which expose the patient to ionizing radiation. MRI radiation-free technique has never been used so far to extract the human bronchi because of insufficient signal and contrast. We propose in this article a processing chain to perform the 3D segmentation of the human airway tree from MR image. From this segmentation we can extract quantitative measurements on the airway, similarly to CT scan segmented airway trees. Finally we will present the results of a clinical study to prove those measurements are relevant. Replacing CT scan by MRI to assess airway diseases is mandatory for many applications (young people imaging, repeated measurements to follow-up a response to a treatment, ...) for which ionizing acquisition should be avoided. Thus, the results presented in this article open many perspectives related to lung diseases study.

Citation Download Citation

Fabien Baldacci, François Laurent, Patrick Berger, and Gaël Dournes "3D human airway segmentation from high hesolution MR imaging", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410Y (15 March 2019); https://doi.org/10.1117/12.2522948

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