Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty.Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask.The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.

翻译：放射科医生具有不同的培训和临床经验，导致肺结节分割标注存在差异并产生分割不确定性。传统方法通常选择单一标注作为学习目标，或尝试学习包含多种标注的潜在空间。然而，这些方法未能充分利用多标注中蕴含的一致性与分歧信息。本文提出一种不确定性感知注意力机制（UAAM），利用多标注间的一致性与分歧促进更优分割。为此，我们引入多置信度掩膜（MCM），其由低置信度（LC）掩膜和高置信度（HC）掩膜组成。低置信度掩膜标注了分割置信度较低的区域，这些区域中放射科医生可能存在不同的分割选择。基于UAAM，我们进一步设计不确定性引导的多置信度分割网络（UGMCS-Net），包含三个模块：特征提取模块捕获肺结节通用特征，不确定性感知模块生成对应标注并集、交集和标注集的三种特征，以及交并约束模块通过三种特征之间的距离平衡最终分割与MCM的预测。为全面展示方法性能，我们在LIDC-IDRI数据集上提出复杂结节验证方案，测试UGMCS-Net对常规方法难以分割的肺结节的分割性能。实验结果表明，本方法能显著提升对传统方法难以分割结节的分割性能。