论文标题
使用伤口分割和重建来生成3D生物打印斑块，以治疗糖尿病足溃疡
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
论文作者
论文摘要
我们引入了AID REGEN，这是一种新型系统，该系统生成了3D伤口模型，将2D语义分割与3D重建相结合，以便可以在手术期间通过3D Bio-rinkers打印它们以治疗糖尿病足溃疡（DFUS）。 AID无缝地将完整的管道绑定，其中包括RGB-D图像捕获，语义分割，边界引导点云处理，3D模型重建和3D可打印的G代码生成，将其用于可以在包装盒外使用的单个系统。我们开发了一种多阶段数据预处理方法来处理小型和不平衡的DFU图像数据集。 AID REGEN的人类在循环机器学习界面使临床医生不仅可以创建3D再生贴片，还可以使用一些触摸互动，而且可以自定义和确认伤口界限。正如我们的实验所证明的那样，我们的模型优于先前的伤口分割模型和我们的重建算法能够以令人信服的精度生成3D伤口模型。我们进一步对真正的DFU患者进行了案例研究，并证明了Regen在治疗DFU伤口中的有效性。
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.
