Automatic segmentation and measurement system of 3D point cloud images based on RGB-D camera for rat wounds

Background and Objective

Accurate wound segmentation is an indispensable prerequisite for automated wound size measurement and healing process monitoring. Traditional assessment methods rely on time-consuming manual analysis, which are inefficient and suceptible to subjective judgment.

Methods

To simulate the changes of human wound healing, in this study, 3D point cloud data of 927 rat wounds at different healing stages were acquired, and a 3D point cloud segmentation model based on the improved PointNet++ was proposed to segment the wound area and get its 3D shape. Twenty-eight groups of simulated wounds were constructed, and reliable wound volumes were obtained by calculating the convex hulls of the simulated wound point clouds and regressing the convex hull volume with the actual wound volume.

Result

The improved model achieves 91.4 % in the intersection and mean intersection over union (mIoU) for wound segmentation, which is 1.57 % and 1.18 % higher than that of PointNet and the original PointNet++ model. Further, the volume of the convex hull was used to perform a regression analysis with the real volume of the simulated wound, and then the wound volume of the rats was calculated, in which the Pearson’s correlation coefficient was 0.996 and the R-square was 0.993, which indicated that there was a significant linear relationship between the two and proved that the wound volume measurements possessed a high degree of reliability.

Conclusion

This method acquires 3D wound morphology post-segmentation and provides accurate volume measurements, enhancing wound treatment monitoring and advancing 3D point cloud use in clinical settings.