A low-cost imaging platform for quantitative characterisation of scaffold surface macrotopography with potential...

Craniofacial bone regeneration is a demanding application of tissue engineering (TE) in which scaffold architecture-pore size, shape and spatial distribution-is a determinant of regenerative success, so that reliable geometric quality control is a prerequisite for translation. Conventional assessment of printed scaffold geometry is largely manual, time-consuming and subject to operator variability. Here we present a low-cost, semi-automated imaging platform that combines custom 3D-printed hardware-a 12.3 MP Raspberry Pi High Quality Camera with a 6 mm CS-mount lens, an adjustable monopod and a ring illuminator-with a dedicated image-processing pipeline. The system acquires a single zenithal image of a scaffold and segments it to quantify the number, area, perimeter and compactness of the pores of the uppermost printed layer; it therefore characterises two-dimensional surface macrotopography and does not resolve internal three-dimensional architecture or pore interconnectivity. On a printed reference grid the platform was highly repeatable (coefficient of variation, CV = 1.33%) but systematically underestimated pore area by 11.6%, a reproducible bias that can be removed by calibration. On a 3D-printed PLA scaffold, repeated acquisitions agreed to within CV = 0.01-2.48% (N = 5), whereas manual measurement of the same specimen by three experienced operators gave an inter-user CV of 11.5-22.8% and an intra-user CV of 0-12.7%. Feasibility was further demonstrated on two silica-gelatin hybrid bioink scaffolds and on a silica-based scaffold with non-linear pore boundaries. The total hardware cost is approximately €170. By reducing user-dependent variability under the conditions tested, and at a cost accessible to standard laboratories, the platform provides a practical quality-control tool for scaffold fabrication, with potential application to craniofacial tissue engineering.