Research on melanoma image segmentation by incorporating medical prior knowledge

BACKGROUND: Melanoma image segmentation has important clinical value in the diagnosis and treatment of skin diseases. However, due to the difficulty of obtaining data sets, and the sample imbalance, the quality of melanoma image data sets is low, which reduces the accuracy and the effectiveness of computer aided diagnosis of melanoma image. OBJECTIVE: In this work, a method of melanoma image segmentation by incorporating medical prior knowledge is proposed to improve the fidelity of melanoma image segmentation. METHODS: Anatomical analysis of the melanoma image reveal the star shape of the melanoma image, which can be encoded into the loss function of the UNet model as a prior knowledge. RESULTS: Our experimental results on the ISIC-2017 data set demonstrate that the model by incorporating medical prior knowledge obtain a mIoU (Mean Intersection over Union) of 87.41%, a Dice Similarity Coefficient of 93.49%. CONCLUSION: Therefore, the model by incorporating medical prior knowledge achieve the first rank in the segmentation task comparing to other models and has high clinical value.