Huang Wei, Zhang Lei, Shu Xin, Wang Zizhou, Yi Zhang
IEEE J Biomed Health Inform. 2024 Dec;28(12):7175-7183. doi: 10.1109/JBHI.2024.3451210. Epub 2024 Dec 5.
Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator preference has garnered great interest, and several methods have been proposed in the past two years. However, the existing methods completely ignore the potential correlation between annotations, such as complementary and discriminative information. In this work, the Adaptive annotation CorrelaTion based multI-annOtation LearNing (ACTION) method is proposed for calibrated medical image segmentation. ACTION employs consensus feature learning and dynamic adaptive weighting to leverage complementary information across annotations and emphasize discriminative information within each annotation based on their correlations, respectively. Meanwhile, memory accumulation-replay is proposed to accumulate the prior knowledge and integrate it into the model to enable the model to accommodate the multi-annotation setting. Two medical image benchmarks with different modalities are utilized to evaluate the performance of ACTION, and extensive experimental results demonstrate that it achieves superior performance compared to several state-of-the-art methods.
医学图像分割是许多临床应用中的一项基础任务，但当前的自动分割方法严重依赖手工标注，而手工标注本质上是主观的，且容易出现标注偏差。最近，对标注者偏好进行建模引起了极大关注，在过去两年中已经提出了几种方法。然而，现有方法完全忽略了标注之间的潜在相关性，比如互补信息和判别信息。在这项工作中，我们提出了基于自适应标注相关性的多标注学习（ACTION）方法用于校准医学图像分割。ACTION采用共识特征学习和动态自适应加权，分别利用跨标注的互补信息，并基于标注之间的相关性强调每个标注内的判别信息。同时，提出了记忆积累-回放机制来积累先验知识并将其整合到模型中，以使模型能够适应多标注设置。我们利用两个不同模态的医学图像基准来评估ACTION的性能，大量实验结果表明，与几种当前最先进的方法相比，它取得了更优的性能。
