Quantitative computed tomography (QCT) indices calculated on paired inspiratory/expiratory multidetector computer tomography (MDCT) can deliver valuable information about functional changes in airway diseases like cystic fibrosis (CF). Air trapping is an important early sign of CF which can only be quantified on expiratory CTs. An accurate lobe segmentation is needed for a regional analysis. Direct lobe segmentation (DLS) is more challenging to perform on expiratory CT images than on inspiratory images. We suggest a registration-based lobe segmentation (RLS) procedure for expiratory CTs if paired inspiratory/expiratory CTs are available. Firstly, our existing fully automated lobe segmentation algorithm was applied to the inspiratory images. Secondly, inspiratory and expiratory images were aligned by a deformable image registration algorithm. Thirdly, the calculated transformation between inspiratory and expiratory images was applied on the lobe segmentation determined on the inspiratory images. Finally, the transferred lobe segmentation was slightly adjusted to the expiratory CT. Validation of the procedure was performed on 128 paired inspiratory/expiratory CTs. The scans were acquired from 16 children with mild CF at 4 time points reconstructed with two different kernels. 6 lobes were segmented, the lingula was treated as separate lobe. We validated the registration-based lobe masks against manually corrected lobe masks. The mean spatial overlap (Dice Index) for DLS was 0.97±0.02 on the inspiratory CTs, and 0.82 ± 0.09 on expiratory CTs, determined in a previous study. In the present study the overlap was significantly improved for the expiratory CTs by the new RLS approach to 0.91 ± 0.05 (p < 2.2e − 16). This significant improvement brings the quality of lung lobe segmentation on expiratory CTs closer to the already very good lobe segmentation results on inspiratory CTs by DLS, thus reducing the need for manual post-processing.

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503I (13 March 2019); doi: 10.1117/12.2513174

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)