  We introduce AiD Regen, a novel system that generates 3D wound models
combining 2D semantic segmentation with 3D reconstruction so that they can be
printed via 3D bio-printers during the surgery to treat diabetic foot ulcers
(DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D
image capturing, semantic segmentation, boundary-guided point-cloud processing,
3D model reconstruction, and 3D printable G-code generation, into a single
system that can be used out of the box. We developed a multi-stage data
preprocessing method to handle small and unbalanced DFU image datasets. AiD
Regen's human-in-the-loop machine learning interface enables clinicians to not
only create 3D regenerative patches with just a few touch interactions but also
customize and confirm wound boundaries. As evidenced by our experiments, our
model outperforms prior wound segmentation models and our reconstruction
algorithm is capable of generating 3D wound models with compelling accuracy. We
further conducted a case study on a real DFU patient and demonstrated the
effectiveness of AiD Regen in treating DFU wounds.
