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A Distance Function based Cascaded Neural Network for accurate Polyps Segmentation and Classification

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In clinical practice, it is often difficult to locate and measure the size of polyps accurately for the follow-up surgical operation decision. In this paper, based on the position constraint between the primary organ and polyps boundary, we propose a U-Net based cascaded neural network for the joint segmentation of the organ of interest and polyps. The constraint on their position relation is further imposed by adding a narrow-band distance function and complimentary dice function to the loss function. Through a series of comparisons and ablation study, the proposed method with the cascaded network architecture and the additional loss functions was validated on an in-house dataset for gallbladder polyps segmentation and classification. It has been demonstrated that the proposed method achieved a significant improvement over conventional U-Net, U-Net++ etc.. Eventually, the pathological type classification based on the segmented polys shows 30% higher accuracy compared to those conventional ResNet based result

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