Multi-step deep neural network for identifying subfascial vessels in a dorsal skinfold window chamber model

Automatic segmentation of blood vessels in the dorsal skinfold window chamber (DWSC) model is a prerequisite for the evaluation of vascular-targeted photodynamic therapy (V-PDT) biological response. Recently, deep learning methods have been widely applied in blood vessel segmentation, but they have difficulty precisely identifying the subfascial vessels. This study proposed a multi-step deep neural network, named the global attention-Xnet (GA-Xnet) model, to precisely segment subfascial vessels in the DSWC model. We first used Hough transform combined with a U-Net model to extract circular regions of interest for image processing. GA step was then employed to obtain global feature learning followed by coarse segmentation for the entire blood vessel image. Secondly, the coarse segmentation of blood vessel images from the GA step and the same number of retinal images from the DRIVE datasets were combined as the mixing sample, inputted into the Xnet step to learn the multiscale feature predicting fine segmentation maps of blood vessels. The data show that the accuracy, sensitivity, and specificity for the segmentation of multiscale blood vessels in the DSWC model are 96.00%, 86.27%, 96.47%, respectively. As a result, the subfascial vessels could be accurately identified, and the connectedness of the vessel skeleton is well preserved. These findings suggest that the proposed multi-step deep neural network helps evaluate the short-term vascular responses in V-PDT.