Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from multiple sources with varying label qualities. However, directly introducing additional data with low-quality noisy labels may mislead the network training and undesirably offset the efficacy provided by those high-quality labels. To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL) framework constructed by a teacher-student architecture and a label self-denoising process to robustly learn segmentation from a small set of high-quality labeled data and plentiful low-quality noisy labeled data. Particularly, such a synergistic framework is capable of simultaneously and robustly exploiting (i) the additional dark knowledge inside the images of low-quality labeled set via perturbation-based unsupervised consistency, and (ii) the productive information of their low-quality noisy labels via explicit label refinement. Comprehensive experiments on left atrium segmentation with simulated noisy labels and hepatic and retinal vessel segmentation with real-world noisy labels demonstrate the superior segmentation performance of our approach as well as its effectiveness on label denoising.

中文翻译：

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抗噪声标签干扰：平均教师辅助的医学图像分割置信学习


手动分割医学图像需要专业知识，耗时且费力。从专家那里获取大量高质量的标记数据通常是不可行的。不幸的是，如果没有足够的高质量像素级标签，通常的数据驱动的基于学习的分割方法往往会因训练不足而苦苦挣扎。因此，我们经常被迫从具有不同标签质量的多个来源收集额外的标签数据。然而，直接引入具有低质量噪声标签的附加数据可能会误导网络训练，并不利地抵消这些高质量标签提供的功效。为了解决这个问题，我们提出了一种由师生架构和标签自去噪过程构建的均值教师辅助置信学习（MTCL）框架，可以从一小组高质量标记数据和大量低质量标记数据中稳健地学习分割。 -质量噪声标记数据。特别是，这样的协同框架能够同时稳健地利用（i）通过基于扰动的无监督一致性，低质量标记集图像内的额外暗知识，以及（ii）低质量噪声标签的生产信息通过显式标签细化。使用模拟噪声标签进行左心房分割以及使用真实世界噪声标签进行肝和视网膜血管分割的综合实验证明了我们的方法的卓越分割性能及其在标签去噪方面的有效性。