Precise nodule segmentation in thyroid ultrasound images is important for clinical quantitative analysis and diagnosis. Fully supervised deep learning method can effectively extract representative features from nodules and background. Despite the great success, deep learning–based segmentation methods still face a critical hindrance: the difficulty in acquiring sufficient training data due to high annotation costs. To this end, we propose a weakly supervised framework called uncertainty to fine generative adversarial network (U2F-GAN) for nodule segmentation in thyroid ultrasound images that exploits only a handful of rough bounding box annotations to successfully generate reliable labels from these weak supervisions. Based on feature-matching GAN, the proposed method alternates between generating masks and learning a segmentation network in an adversarial manner. Super-pixel processing mechanism is adopted to reflect low-level image structure features for learning and inferring semantic segmentation, which largely improve the efficiency of training process. In addition, we introduce a similarity comparison module and a distributed loss function with constraints to effectively remove noise in localization annotations and enhance the generalization capability of the network, thus strengthen the overall segmentation performance. Compared to existing weakly supervised approaches, our proposed U2F-GAN yields a significant performance boost. The segmentation results are also comparable to fully supervised methods, but the annotation burden is much lower. Also, the training speed of the network model is much faster than other methods with weak supervisions, which enables the network to be updated in time, thus is beneficial to high-throughput medical image setting.

甲状腺超声图像中精确的结节分割对于临床定量分析和诊断很重要。全监督深度学习方法可以有效地从结节和背景中提取具有代表性的特征。尽管取得了巨大的成功，但基于深度学习的分割方法仍然面临一个关键障碍：由于注释成本高，难以获得足够的训练数据。为此，我们提出了一种称为不确定性精细生成对抗网络 (U2F-GAN) 的弱监督框架，用于甲状腺超声图像中的结节分割，该框架仅利用少数粗边界框注释从这些弱监督中成功生成可靠标签。基于特征匹配的GAN，所提出的方法在生成掩码和以对抗方式学习分割网络之间交替。采用超像素处理机制，反映低层图像结构特征，用于学习和推断语义分割，大大提高了训练过程的效率。此外，我们引入了相似性比较模块和带约束的分布式损失函数，以有效去除定位注释中的噪声并增强网络的泛化能力，从而增强整体分割性能。与现有的弱监督方法相比，我们提出的 U2F-GAN 产生了显着的性能提升。分割结果也与全监督方法相当，但注释负担要低得多。还，