Fig. 1: ML-based single-vessel analysis method.

a, This approach includes multiple steps. First, the images containing spatial distribution of vessel and protein nanoprobes were acquired following systemic administration via the tail vein of tumour-bearing mice. Next, manually annotated images were trained using a deep neural network. The collected images from various tumour tissues were automatically segmented using the trained models. Finally, the features of input images were automatically segmented and quantitatively analysed. b, A detailed workflow for ML-based automatic image segmentation and quantitative analysis. During step 1, the images of tumour tissues were preprocessed. During step 2, two-channel images including vessel channel and nanoprobe channel were separated and their boundaries were manually annotated. The ML-based models were established by training of manually annotated images using the U-net convolutional neural network. During step 3, using the established image segmentation models, a large number of collected images were input for machine automatic segmentation. The quantification information was also automatically output in terms of manually setting indices.