Abstract
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: (1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. (2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents' erasing process precisely, thereby avoiding both under- and over-segmentation. (3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms.
Abstract (translated)
在2D乳腺超声（BUS）和3D自动乳腺超声（ABUS）中对结节的准确分割对于临床诊断和治疗计划至关重要。因此，开发一种用于结节分割的自动化系统可以增强用户的独立性，并加速临床分析。与完全监督学习相比，弱监督分割（WSS）能够简化繁琐且复杂的标注过程。然而，现有的WSS方法在实现精确的结节分割方面面临挑战，因为它们往往依赖于不准确的激活图或低效的伪标签生成算法。本研究介绍了一种基于多智能体强化学习的新型WSS框架，称为Flip Learning（翻转学习），该框架仅依靠2D/3D边界框来实现精确分割。具体而言，多个智能体被用于从边界框中擦除目标以促进分类标签翻转，而擦除区域则作为预测分割掩模。 这项研究的主要贡献如下： 1. 采用基于超像素/超体积的方法编码标准化环境，捕捉边界先验并加速学习过程。 2. 引入了三种精心设计的奖励机制，包括分类得分奖励和两种强度分布奖励，以精确引导智能体的擦除过程，并避免过度分割或不足分割。 3. 实施了一种渐进式课程学习策略，使智能体能够逐步面对更具挑战性的环境交互，从而提高学习效率。 在大型内部BUS和ABUS数据集上进行了广泛的验证后，我们的Flip Learning方法优于现有的WSS方法及基础模型，并且与完全监督的算法性能相当。
URL
https://arxiv.org/abs/2503.20685
