Wang Yifeng, Luo Luyang, Wu Mingxiang, Wang Qiong, Chen Hao
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Med Image Anal. 2025 Apr;101:103489. doi: 10.1016/j.media.2025.103489. Epub 2025 Feb 8.
Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. However, learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of the annotations. In this paper, we proposed an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guided the training process by uncertainty estimation at both the pixel and the image levels. First, we developed an annotation uncertainty estimation module (AUEM) to estimate the pixel-wise uncertainty of each annotation, which then guided the network to learn from reliable pixels by a weighted segmentation loss. Second, a quality assessment module (QAM) was proposed to assess the image-level quality of the input samples based on the former estimated annotation uncertainties. Furthermore, instead of discarding the low-quality samples, we introduced an auxiliary predictor to learn from them and thus ensured the preservation of their representation knowledge in the backbone without directly accumulating errors within the primary predictor. Extensive experiments demonstrated the effectiveness and feasibility of our proposed UMA-Net on various datasets, including 2D chest X-ray segmentation dataset, 2D fundus image segmentation dataset, 3D breast DCE-MRI segmentation dataset, and the QUBIQ multi-task segmentation dataset. Code will be released at https://github.com/wangjin2945/UMA-Net.
从多个独立来源收集注释可以减轻单个来源潜在噪声和偏差的影响，这在医学图像分割中是一种常见做法。然而，由于注释差异带来的不确定性，从多源注释中学习分割网络仍然是一个挑战。在本文中，我们提出了一种不确定性引导的多源注释网络（UMA-Net），它在像素和图像级别通过不确定性估计来指导训练过程。首先，我们开发了一个注释不确定性估计模块（AUEM）来估计每个注释的逐像素不确定性，然后通过加权分割损失引导网络从可靠像素中学习。其次，提出了一个质量评估模块（QAM），基于先前估计的注释不确定性来评估输入样本的图像级质量。此外，我们不是丢弃低质量样本，而是引入了一个辅助预测器来从它们中学习，从而确保在主干中保留它们的表示知识，而不会在主预测器中直接累积错误。大量实验证明了我们提出的UMA-Net在各种数据集上的有效性和可行性，包括二维胸部X光分割数据集、二维眼底图像分割数据集、三维乳腺动态对比增强磁共振成像分割数据集和QUBIQ多任务分割数据集。代码将在https://github.com/wangjin2945/UMA-Net上发布。
