DualPSNet: A Discrepant-Annotations-Driven Framework for Medical Image Segmentation from Dual-Polygon Supervision

The performance of medical image segmentation is critical for subsequent analysis and decision-making in clinical practice. Although great successes have been achieved by employing deep neural networks trained with large-scale annotated datasets, the cost of collecting such datasets is usually expensive, which makes the application of these methods challenging. To address this difficulty, we propose a double-branch framework from the perspective of integrating data annotation and model construction. For the data annotation, to preserve more information as well as high efficiency, a dual polygon annotation approach is employed from the view of sparse annotating and multi-rater learning. In the generated dual polygon annotations, a larger annotation contains all the information about the lesion area, while the smaller annotation is inside the lesion area as much as possible and does not contain any background information. For the model construction, to utilize the polygon annotations efficiently, a framework included one encoder and two different decoders is used to learn agreement information between two annotations in an adversarial manner, while facilitating the learning of disagreement information. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on the ISIC2017, ISIC2018, and KvasirSeg datasets. Experimental results demonstrate that the proposed method can achieve comparable or even superior performance than the fully-supervised methods.