Supplementary Material for: Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation

Background: Pressurized myography is useful for the assessment of small artery structures and function. However, this procedure requires technical expertise for sample preparation and effort to choose an appropriate sized artery. In this study, we developed an automatic artery/vein differentiation and a size measurement system utilizing machine learning algorithms. Methods and Results: We used 654 independent mouse mesenteric artery images for model training. The model yielded an Intersection-over-Union of 0.744 ± 0.031 and a Dice coefficient of 0.881 ± 0.016. The vessel size and lumen size calculated from the predicted vessel contours demonstrated a strong linear correlation with manually determined vessel sizes (R = 0.722 ± 0.048, p R = 0.908 ± 0.027, p R = 0.832, p Conclusion: The U-Net-based image analysis method could streamline the experimental approach.