earticle

영어

Colonoscopy is the most effective examination way to detect colon polyps, which are highly related to colorectal cancer. Consequently, it is an important step to segment the poly accurately for diagnosis in clinical practice. However, most prior works focus on performance improvement using deep convolutional neural networks while the discrepancy between the training dataset and the test dataset is ignored. These distribution discrepancies may lead to the model overfitting the training dataset and lacking generalizability on unseen target domains. To alleviate this issue, we propose a Randomized Local Illumination Enhancement Network for polyp image segmentation. Specifically, we first employ an illumination decomposition network to decompose the input images into an illumination component and a reflectance component. The illumination component is augmented by randomly selected local illumination. Then the randomized local illumination-enhanced images are obtained by combining the augmented illumination and the reflectance, which are fed as the input of the segmentation network for improving the model generalizability. We conduct both quantitative and qualitative experiments on four polyp segmentation datasets. The satisfying results demonstrate the effectiveness of our proposed approach in the improvement of model generalizability on unseen data.