Automatic 3D Segmentation of Hydrogel Scaffolds Based on PBI-µCT

Hydrogel scaffolds are a promising biomaterial used in tissue engineering and regenerative medicine. Scaffolds can be constructed using 3D bioprinting techniques which allow for intricate architectures at the site of injury. However, 3D segmentation of hydrogel scaffolds remains a challenge due to the low-density of hydrogels exhibiting poor image contrast. One promising method is to use synchrotron radiation (SR) propagation-based imaging (PBI) microcomputed tomography (μCT). This method shows the phase shift of X-rays between sample and detector with strong edge enhancement. Quantitatively, this phase shift can be calculated using phase retrieval (PR) algorithm which converts the edge enhancement into area where the X-ray propagated through the sample but will reduce sharpness between across boundaries. Alternatively, image denoising, e.g., Noise2Inverse (N2I), can be used to maximize the edge enhancement but does not represent the area. Thus, SR-PBI-μCT data can be processed in complementary ways. A convolutional neural network (CNN) can be trained to learn the complementary area and edge information which allow for more accurately represented result than either method alone. This provides efficient 3D segmentation without manual input or pre-existing reference.