iPhone X and Deep Learning in Wound Assessment

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Presentation transcript:

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

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.

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.

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

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

Results 2D FCN Segmentation 3D PointNet Segmentation

Training Result 2D FCN Segmentation 3D PointNet Segmentation

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.

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.

Thank You! Q&A