RESUMO
This study developed an automatic detection algorithm of vessel and skin regions in a transversal ultrasonography image on the arm. We also developed an algorithm to generate a 3D model from detected areas to assist vein puncture. In the algorithm, the vessel's candidate regions in the ultrasonography image were detected using U-Net or Mask R-CNN, which are a kind of deep learning method for segmentation. Then vessel regions were selected among the candidates based on continuous properties in an image sequence. The skin regions were also detected. The 3D polygon data was created from paired pixels in sequential images. The experiments demonstrated that Mask R-CNN could correctly estimate the branch of vessel which were difficult to identify accurate region separately using U-Net, and achieved an overall IoU of 80%. The confirmation experiment of 3D model demonstrated that generated model have enough feasibility for assessment of appropriate veins and locations for puncture.Clinical relevance-The developed 3D model generation from ultrasonography images will be useful for support to identify the appropriate veins for puncture.
