分阶段体素级深度强化学习用于带有噪声标注的3D医学图像分割
Staged Voxel-Level Deep Reinforcement Learning for 3D Medical Image Segmentation with Noisy Annotations
AI总结：
本文提出分阶段体素级深度强化学习框架，通过动态迭代更新策略和新颖的强化学习方法，在噪声标注条件下提升医学图像分割的准确性和鲁棒性。
摘要：
深度学习在医学图像分割中取得了显著进展。目前，获得准确分割结果严重依赖于大规模高质量标注数据集。然而，由于器官在医学图像中的复杂形态结构和不同标注者之间的差异，噪声标注经常出现，这会显著限制分割模型的效果。受医学影像标注者在分割过程中可以根据先验知识纠正标注错误的启发，我们提出了一种端到端的分阶段体素级深度强化学习（SVL-DRL）框架，用于在噪声标注下实现稳健的医学图像分割。该框架采用动态迭代更新策略，自动减轻错误标签的影响，而无需人工干预。SVL-DRL相较于现有方法的关键改进包括：i）将噪声标注作为体素依赖问题进行建模，并通过新颖的分阶段强化学习框架解决，确保模型的鲁棒收敛；ii）引入体素级异步优势演员评论员（vA3C）模块，将每个体素视为一个自主代理，允许每个代理在训练过程中动态优化自身状态表示，从而直接减轻错误标签的影响；iii）设计新的动作空间以及复合奖励函数，该函数战略性地结合Dice值和空间连续性度量，显著提高分割准确性同时保持语义完整性。在三个公开医学图像数据集上的实验表明，在各种实验设置下实现了最先进的性能，平均Dice和IoU分数提高了超过3%。
英文摘要：
Deep learning has achieved significant advancements in medical image segmentation. Currently, obtaining accurate segmentation outcomes is critically reliant on large-scale datasets with high-quality annotations. However, noisy annotations are frequently encountered owing to the complex morphological structures of organs in medical images and variations among different annotators, which can substantially limit the efficacy of segmentation models. Motivated by the fact that medical imaging annotator can correct labeling errors during segmentation based on prior knowledge, we propose an end-to-end Staged Voxel-Level Deep Reinforcement Learning (SVL-DRL) framework for robust medical image segmentation under noisy annotations. This framework employs a dynamic iterative update strategy to automatically mitigate the impact of erroneous labels without requiring manual intervention. The key advancements of SVL-DRL over existing works include: i) formulating noisy annotations as a voxel-dependent problem and addressing it through a novel staged reinforcement learning framework which guarantees robust model convergence; ii) incorporating a voxel-level asynchronous advantage actor-critic (vA3C) module that conceptualizes each voxel as an autonomous agent, which allows each agent to dynamically refine its own state representation during training, thereby directly mitigating the influence of erroneous labels; iii) designing a novel action space for the agents, along with a composite reward function that strategically combines the Dice value and a spatial continuity metric to significantly boost segmentation accuracy while maintain semantic integrity. Experiments on three public medical image datasets demonstrates State-of-The-Art (SoTA) performance under various experimental settings, with an average improvement of over 3\% in both Dice and IoU scores.
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