Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers

TL;DR

AiD Regen is an integrated system that captures, segments, reconstructs, and prints 3D wound models for diabetic foot ulcers, streamlining surgical treatment with minimal user interaction.

Contribution

It introduces a comprehensive pipeline combining segmentation, reconstruction, and 3D printing for DFU treatment, with a novel multi-stage preprocessing and human-in-the-loop interface.

Findings

Outperforms prior wound segmentation models

Generates accurate 3D wound models

Proven effective in a real patient case study

Abstract

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.