Robust segmenting with noisy labels is an important problem in medical imaging due to the difficulty of acquiring high-quality annotations. Despite the enormous success of recent developments, these developments still require multiple networks to construct their frameworks and focus on limited application scenarios, which leads to inflexibility in practical applications. They also do not explicitly consider the coarse boundary label problem, which results in sub-optimal results. To overcome these challenges, we propose a novel Simultaneous Edge Alignment and Memory-Assisted Learning (SEAMAL) framework for noisy-label robust segmentation. It achieves single-network robust learning, which is applicable for both 2D and 3D segmentation, in both Set-HQ-knowable and Set-HQ-agnostic scenarios. Specifically, to achieve single-model noise robustness, we design a Memory-assisted Selection and Correction module (MSC) that utilizes predictive history consistency from the Prediction Memory Bank to distinguish between reliable and non-reliable labels pixel-wisely, and that updates the reliable ones at the superpixel level. To overcome the coarse boundary label problem, which is common in practice, and to better utilize shape-relevant information at the boundary, we propose an Edge Detection Branch (EDB) that explicitly learns the boundary via an edge detection layer with only slight additional computational cost, and we improve the sharpness and precision of the boundary with a thinning loss. Extensive experiments verify that SEAMAL outperforms previous works significantly.

中文翻译：

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学习单个网络，实现具有噪声标签的稳健医学图像分割


由于难以获得高质量的注释，使用噪声标签进行稳健分割是医学成像中的一个重要问题。尽管最近的发展取得了巨大的成功，但这些发展仍然需要多网络来构建框架，并专注于有限的应用场景，这导致了实际应用的不灵活性。他们也没有明确考虑粗略边界标签问题，这会导致次优结果。为了克服这些挑战，我们提出了一种新的同步边缘对齐和记忆辅助学习 （SEAMAL） 框架，用于噪声标签鲁棒分割。它实现了单网络稳健学习，适用于 Set-HQ 可知和 Set-HQ 不可知场景中的 2D 和 3D 分割。具体来说，为了实现单模型噪声鲁棒性，我们设计了一个内存辅助选择和校正模块 （MSC），该模块利用预测内存库的预测历史一致性来逐像素区分可靠和不可靠的标签，并在超像素级别更新可靠的标签。为了克服实践中常见的粗略边界标签问题，并更好地利用边界处的形状相关信息，我们提出了一个边缘检测分支 （EDB），它通过边缘检测层显式学习边界，只需少量的额外计算成本，并且我们提高了边界的清晰度和精度，但损失变薄。广泛的实验验证了 SEAMAL 的性能明显优于以前的工作。