Nasal morphology and health disparities in asthma: A study assessing semi-automated tools for processing computed tomography scans in 3D morphometric research

Purpose: As the nasal complex is predominantly responsible for respiratory heat and moisture exchange, it has been suggested that abnormal nasal anatomy may predisposes certain individuals to asthma. Recent research into anatomical etiologies of asthma have increasingly turned to medical imaging modalities(e.g., CT/MRI scans) to quantify 3D nasal anatomy across large samples(n > 10,000) of asthmatic patients. However, lack of automated and semi-automated procedures for scan processing has represented a significant obstacle for such anatomical studies. Accordingly, the purpose of this project was to evaluate the applicability of a newly developed subroutine for the NIH-funded 3D Slicer software program designed to semi-automate the alignment of cranial CT-scans into the Frankfort Horizontal plane for subsequent morphometric assessment. Methods: Our study sample consisted of 10 high-resolution cranial CT scans of dried skulls. These CT scans were first processed using traditional methods for aligning the cranium into the Frankfort Horizontal plane, followed by a second trial employing a new python-based script alignment subroutine for the "SlicerMorph" extension module of 3D Slicer. Results: Overall, the automated subroutine showed a significant improvement in image processing times, reducing alignment time for a single scan by approximately 60% from 50-60 minutes down to 15-20 minutes. Furthermore, the accuracy of alignment was found to be substantially improved. Conclusion: This novel subroutine will allow future researchers to efficiently process segmented scans of human crania while decreasing observer error yet increasing the morphometric accuracy.