1. iPhone X and Deep Learning in Wound Assessment
Zhuoran Hao, Jingtao Yang, Shaodi Xu
Advisor: Zeyun Yu Department of Computer Science

2. Background
Problem Statement: The current wound assessment needs a standard method of measuring wound area and depth to serve as the foundation of a primary diagnosis.
Wound depth and area, if accurately measured, can serve as the treatment baseline to measure healing progress
Current measuring methods may yield different results depending on the practitioner.

3. 2D FCN-Segmentation
We use FCN (Fully Convolutional Networks) for 2D wound Image segmentation as our auxiliary research since there is a comprehensive medical image dataset to training our neural network. And we need this work to compare with our 3D Image Segmentation.

4. iPhone X scan 3D object
Depth Image will be upload to server

5. 3D PointNet-Segmentation
PointNet consumes raw unordered point cloud without voxelization or rendering

6. Results
2D FCN Segmentation
3D PointNet Segmentation

7. Training Result
2D FCN Segmentation
3D PointNet Segmentation

8. Conclusion
PointNet is a novel deep neural network which directly consumes point cloud. It can be successfully applied to 3D tasks such as 3D segmentation.
With iPhone X depth camera, we can generate object 3D model more faster and easily, and it could be a game changer if we apply this on Healthcare.

9. Future Work
We use PointNet’s own dataset for training since our iOS application was just finished and we can not gather sufficiently medical 3D model in a short time. Thus the further goal for our research is to build our wound dataset with iPhone X and we also want to build a streaming 3D server for capture 3D information in real-time.

10. Thank You!
Q&A