Automated 3d camera measures thickness of bioprinted tissue constructs
An Automated Thickness Evaluation Procedure Using an Integrated Structured Light 3D Camera in a Robotic Bioprinting Framework
Robotics
Summary
Measuring how thick bioprinted tissues are is important for growing cells properly, but current methods lack precision and automation. This paper presents a fully automated way to measure the thickness of tissue shapes made by bioprinters using a 3D camera that scans the surface and color images to detect the tissue boundaries. The authors combine image segmentation and robot data with 3D point clouds to get very accurate thickness measurements even for complex shapes. They tested their method in virtual simulations and on real bioprinted samples, achieving errors below 0.06 millimeters.
What this means in practice
- •For bioprinting engineers: Measure thickness of complex bioprinted tissues automatically to improve quality control in manufacturing.
- •For robotics system integrators: Incorporate 3D vision-based thickness measurement into robotic setups for precise control of printed biological constructs.
- •For 3d printing service providers: Offer enhanced tissue construct verification services using automated 3D thickness evaluation to assure client specifications.$Commercial implications: Enables sale of automated thickness inspection products for bioprinting companies seeking quality assurance solutions.
Authors
Ehsan Zobeidi, Omid Rezayof, Farshid Alambeigi
Abstract
Bioprinting is emerging as a tissue engineering technique to replace common treatment methods for large scale injuries. While thickness of the BioPrinted Constructs (BPCs) have shown to be of importance in the cell maturation and integration, the literature lacks a robust, automated, and quantitative method for measuring these metrics. In this paper, we propose a fully automated vision-based method for measuring the thickness of the BPCs with complex geometries. Leveraging the point cloud and RGB images of a structured light 3D camera, our proposed method performs an image-based segmentation for delineating the BPCs from the RGB images, accompanied by novel geometry-based thickness measurement algorithms performed on the point cloud scans. These algorithms combine the segmentation mask with the robot's forward kinematics data and a 3D point cloud scan to precisely measure the aforementioned metrics for complex-shaped BPCs. The proposed method was evaluated in simulation and experimental studies. In simulation studies, the algorithms were used to measure the thickness of some virtually created BPCs with known thickness. The comparison between the measured and true thicknesses demonstrates the high accuracy of the proposed method, achieving mean absolute errors between 0.025 mm and 0.057 mm in simulation at a spatial resolution of 0.1 mm x 0.1 mm per pixel. Furthermore, we successfully deployed the algorithms on our robotic bioprinting setup utilizing a structure light 3D camera, where complex patterns were printed and the developed methods utilized to accurately measure the thickness of printed BPCs.