基于混合监督的标签填充方法用于噪声标注下的医学图像分割
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
AI总结：
本文提出LF-Net标签填充框架，通过可信像素子集监督和软标签与标注者特征标签两种混合辅助监督，在仅使用噪声标注的情况下提升医学图像分割精度，并在多个数据集上取得优于现有方法的性能。
摘要：
医学图像分割的成功通常需要大量高质量标签。但由于标注过程通常受到标注者不同技能水平和个体特征的影响，不同标注者提供的估计掩膜通常存在较高的标注者间差异。本文提出了一种简单而有效的标签填充框架，称为LF-Net，在训练期间仅给定噪声标注即可预测真实分割标签。标签填充的基本思想是：用具有可信标签的像素子集来监督分割模型，同时通过混合监督填充其他像素的标签。更具体地说，我们提出了一种合格多数投票策略，即设计一种阈值投票方案来建模标注者之间的一致性，并将所选像素子集的多数投票标签作为监督。为了填充其他像素的标签，提出了两种类型的混合辅助监督：一种是从噪声标注的内在结构中学习的软标签，另一种是标注者特征标签，用于传播个体标注者的特征信息。LF-Net具有两个主要优点。1）使用可信像素进行训练引入了具有置信监督的训练，引导真实标签学习的方向。2）两种类型的混合监督可防止网络在仅由像素子集监督时出现过拟合问题，并保证与真实标签的高保真度。在五种不同成像模态的数据集上的结果表明，与最先进的方法相比，我们的LF-Net在所有数据集上都提高了分割精度，在MS病灶分割中DSC甚至提高了7%。
英文摘要：
The success of medical image segmentation usually requires a large number of high-quality labels. But since the labeling process is usually affected by the raters' varying skill levels and characteristics, the estimated masks provided by different raters usually suffer from high inter-rater variability. In this paper, we propose a simple yet effective Label Filling framework, termed as LF-Net, predicting the groundtruth segmentation label given only noisy annotations during training. The fundamental idea of label filling is to supervise the segmentation model by a subset of pixels with trustworthy labels, meanwhile filling labels of other pixels by mixed supervision. More concretely, we propose a qualified majority voting strategy, i.e., a threshold voting scheme is designed to model agreement among raters and the majority-voted labels of the selected subset of pixels are regarded as supervision. To fill labels of other pixels, two types of mixed auxiliary supervision are proposed: a soft label learned from intrinsic structures of noisy annotations, and raters' characteristics labels which propagate individual rater's characteristics information. LF-Net has two main advantages. 1) Training with trustworthy pixels incorporates training with confident supervision, guiding the direction of groundtruth label learning. 2) Two types of mixed supervision prevent over-fitting issues when the network is supervised by a subset of pixels, and guarantee high fidelity with the true label. Results on five datasets of diverse imaging modalities show that our LF-Net boosts segmentation accuracy in all datasets compared with state-of-the-art methods, with even a 7% improvement in DSC for MS lesion segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
