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Sublingual vein is commonly used to diagnose the health status. The width ofmain sublingual veins gives information of the blood circulation. Therefore, itis necessary to segment the main sublingual veins from the tongueautomatically. In general, the dataset in the medical field is small, which isa challenge for training the deep learning model. In order to train the modelwith a small data set, the proposed method for automatically segmenting thesublingual veins is to re-train U-net model with different sets of the limitednumber of labels for the same training images. With pre-knowledge of thesegmentation, the loss of the trained model will be convergence easier. Toimprove the performance of the segmentation further, a novel strategy of dataaugmentation was utilized. The operation for masking output of the model withthe input was randomly switched on or switched off in each training step. Thisapproach will force the model to learn the contrast invariance and avoidoverfitting. Images of dataset were taken with the developed device using eightnear infrared LEDs. The final segmentation results were evaluated on thevalidation dataset by the IoU metric.

Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images