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This project is the follow-up of the development of a module to use InfraRed (IR) sensors in 3D Slicer for medical diagnosis, intended to use for monitoring of foot ulcers in diabetic patients, presented on the 28thPW NA-MIC. This project is proposed as a new stage in the diabetic foot assessment previously worked.
The aim is to integrate an algorithm, which is based on Deep Learning, for foot segmentation using multimodal images (visible and depth-map images). The resulting mask will be applied on thermal images in order to analyze the temperature pattern and detect possible foot ulcers.
Proposed workflow for feet segmentation where the images are represented as squares and the operations as rectangles.The network prediction is used to set the ROI on the depth image, the point cloud, and then a second segmentation was applied to extract geometry models, specifically planes.
Original depth map. This point cloud is dense populated and contains several noisy points.
Region of interest obtained from the segmentation of Deep Learning in the RGB image. The number of points has been reduced, but still contains noise points.
Comparison between the two images mentioned above.
Feet Segmentation module result. The point cloud visualization is just representative and that result has to be represented as image (Work in Progress) to be used as a mask in the thermal image.
 Hernández, A., Arteaga-Marrero, N., Villa, E., Fabelo, H., Callicó, G. M., & Ruiz-Alzola, J. (2019, September). Automatic Segmentation Based on Deep Learning Techniques for Diabetic Foot Monitoring Through Multimodal Images. In International Conference on Image Analysis and Processing (pp. 414-424). Springer, Cham. Avalible from: https://link.springer.com/chapter/10.1007%2F978-3-030-30645-8_38
 Diabetic Foot Extension. Repository with the extension that includes the “SuperBuild” option and the developed module.