Fully automatic smartphone-based photogrammetric 3D modelling of infant’s heads for cranial deformation analysis

Abstract

Image-based and range-based solutions can be used for the acquisition of valuable data in medicine. However, most of these methods are not valid for non-static patients. Cranial deformation is a problem with high prevalence among infants and image-based solutions can be used to assess the degree of deformation and monitor the evolution of patients. However, it is required to deal with infants normal movement during the assessment in order to avoid sedation. Some high-end multiple-sensor image-based solutions allow the achievement of accurate 3D data for medical applications under unpredicted dynamic conditions in consultation. In this paper, a novel, single photogrammetric smartphone-based solution for cranial deformation assessment is presented. A coded cap is placed on the infant’s head and a guided smartphone app is used by the user to acquire the information, that is later processed on a server to obtain the 3D model. The smartphone app is designed to guide users with no knowledge of photogrammetry, computer vision or 3D modelling. The processing is fully automatic offline. The photogrammetric tool is also non-invasive, reacting well with quick and sudden infant’s movements. Therefore, it does not require sedation. This paper tackles the accuracy and repeatability analysis tested both for a single user (intrauser) and multiple non-expert user (interuser) on 3D printed head models. The results allow us to confirm an accuracy below 1.5 mm, which makes the system suitable for clinical practice by medical staff. The basic automatically-derived anthropometric linear magnitudes are also tested obtaining a mean variability of 0.6 ± 0.6 mm for the longitudinal and transversal distances and 1.4 ± 1.3 mm for the maximum perimeter.