标题： 多评分者棱镜：从多个评分者学习自校准的医学图像分割 显示英文标题

标题： Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

摘要： 在医学图像分割中，通常需要收集多位专家的意见以做出最终决策。 这一临床常规有助于减轻个体偏见。 但是，当数据被多次标注时，标准的深度学习模型往往不适用。 本文提出了一种新颖的神经网络框架，称为多评估者棱镜（MrPrism），用于从多个标签中学习医学图像分割。 受迭代半二次优化的启发，提出的MrPrism将以递归的方式结合多评估者置信分配任务和校准分割任务。 在这个递归过程中，MrPrism可以学习观察者间的变异性，同时考虑图像语义属性，并最终收敛到反映观察者间一致性的自校准分割结果。 具体而言，我们提出了会聚棱镜（ConP）和发散棱镜（DivP）来迭代处理这两个任务。 ConP基于DivP估计的多评估者置信图学习校准分割； DivP则根据ConP估计的分割掩码生成多评估者置信图。 实验结果表明，通过递归运行ConP和DivP，两个任务可以实现相互改进。 MrPrism最终收敛的分割结果在广泛的医学图像分割任务上优于最先进的（SOTA）策略。 显示英文摘要

摘要： In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. But when data is multiply annotated, standard deep learning models are often not applicable. In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels. Inspired by the iterative half-quadratic optimization, the proposed MrPrism will combine the multi-rater confidences assignment task and calibrated segmentation task in a recurrent manner. In this recurrent process, MrPrism can learn inter-observer variability taking into account the image semantic properties, and finally converges to a self-calibrated segmentation result reflecting the inter-observer agreement. Specifically, we propose Converging Prism (ConP) and Diverging Prism (DivP) to process the two tasks iteratively. ConP learns calibrated segmentation based on the multi-rater confidence maps estimated by DivP. DivP generates multi-rater confidence maps based on the segmentation masks estimated by ConP. The experimental results show that by recurrently running ConP and DivP, the two tasks can achieve mutual improvement. The final converged segmentation result of MrPrism outperforms state-of-the-art (SOTA) strategies on a wide range of medical image segmentation tasks.