Automated Segmentation of a Motion Mask to Preserve Sliding Motion in Deformable Registration of Thoracic CT
Résumé
Purpose:
Deformable registration generally relies on the assumption that the sought spatial transformation is smooth. Yet, breathing motion involves sliding of the lung with respect to the chest wall, causing a discontinuity in the motion field, and the smoothness assumption can lead to poor matching accuracy. In response, alternative registration methods have been proposed, several of which rely on prior segmentations. We propose an original method for automatically extracting a particular segmentation, called a motion mask, from a CT image of the thorax.
Methods:
The motion mask separates moving from less‐moving regions, conveniently allowing simultaneous estimation of their motion, while providing an interface where sliding occurs. The sought segmentation is subanatomical and based on physiological considerations, rather than organ boundaries. We therefore first extract clear anatomical features from the image, with respect to which the mask is defined. Level sets are then used to obtain smooth surfaces interpolating these features. The resulting procedure comes down to a monitored level set segmentation of binary label images. The method was applied to sixteen inhale‐exhale image pairs. To illustrate the suitability of the motion masks, they were used during deformable registration of the thorax.
Results:
For all patients, the obtained motion masks complied with the physiological requirements and were consistent with respect to patient anatomy between inhale and exhale. Registration using the motion mask resulted in higher matching accuracy for all patients, and the improvement was statistically significant. Registration performance was comparable to that obtained using lung masks when considering the entire lung region, but the use of motion masks led to significantly better matching near the diaphragm and mediastinum, for the bony anatomy and for the trachea. The use of the masks was shown to facilitate the registration, allowing to reduce the complexity of the spatial transformation considerably, while maintaining matching accuracy.
Conclusions:
We proposed an automated segmentation method for obtaining motion masks, capable of facilitating deformable registration of the thorax. The use of motion masks during registration leads to matching accuracies comparable to the use of lung masks for the lung region but motion masks are more suitable when registering the entire thorax.