Fig. 3: Vessel segmentation results—transfer learning study.

a Segmentation performance as a function of network initialization (measured as Matthews correlation coefficient [MCC]) for the VessTestSet (58 holdout WSIs). The ground-truth annotations of structures were generated for segmenting three classes: glomeruli, arterioles, and arteries. The colors represent different transfer learning sources for parameter initialization. Namely, the glomerulus model is the model originally used for glomerular segmentation results in Fig. 2, offering MCC = 0.91, 0.66, and 0.84 for segmenting glomeruli, arteriole, and arteries, respectively. The random model does not use transfer learning for parameter initialization, offering MCC = 0.55, 0.22, and 0.54 in segmenting the three respective compartments. GTEx (genotype-tissue expression) model is a model originally trained to identify the diverse tissue types from the publicly available GTEx tissue WSI dataset (15,989 WSIs with 40 different tissue types), offering MCC = 0.77, 0.44, and 0.62 for the segmenting three respective compartments after transfer learning. ImageNet model uses a model pretrained on the ImageNet dataset, offering MCC = 0.91, 0.66, and 0.86 in segmenting the three respective compartments. Each dot in the box plot represents a WSI. Box plot elements: The plot starts with the median as the centerline. Each successive level outward contains half of the remaining data. Namely, the first two sections out from the centerline contain 50% of the data. After that, the next two sections contain 25% of the data. This continues until we are at the outlier level. Each level out is shaded lighter. We used around 5–8 outliers in each tail. b shows randomly selected crops of WSIs from the holdout set (VessTestSet) with computational segmentations by the model trained based on the ImageNet model as the starting point. The scale bar is 150 µm. c shows randomly selected crops of various types of tissues from GTEx WSIs, computationally segmented using the model trained based on the ImageNet model. Despite being trained only on kidney tissues, the trained model is able to segment arteries and arterioles in diverse tissue types. We also note that the GTEx slides are autopsy tissues scanned at 20X, and the training set for this study VessTrainSet was scanned at 40X, and did not contain autopsy tissue WSIs. The scale bar is 300 µm.