Automatic medical image segmentation plays a crucial role in computer aided diagnosis. However, fully supervised learning approaches often require extensive and labor-intensive annotation efforts. To address this challenge, weakly supervised learning methods, particularly those using extreme points as supervisory signals, have the potential to offer an effective solution. In this paper, we introduce Deep Extreme Point Tracing (DEPT) integrated with Feature-Guided Extreme Point Masking (FGEPM) algorithm for ultrasound image segmentation. Notably, our method generates pseudo labels by identifying the lowest-cost path that connects all extreme points on the feature map-based cost matrix. Additionally, an iterative training strategy is proposed to refine pseudo labels progressively, enabling continuous network improvement. Experimental results on two public datasets demonstrate the effectiveness of our proposed method. The performance of our method approaches that of the fully supervised method and outperforms several existing weakly supervised methods.

翻译：自动医学图像分割在计算机辅助诊断中起着至关重要的作用。然而，全监督学习方法通常需要大量且劳动密集型的标注工作。为应对这一挑战，弱监督学习方法，特别是那些使用极值点作为监督信号的方法，有望提供一种有效的解决方案。本文提出了一种结合特征引导极值点掩码（FGEPM）算法的深度极值点追踪（DEPT）方法，用于超声图像分割。值得注意的是，我们的方法通过在基于特征图的代价矩阵上识别连接所有极值点的最低成本路径来生成伪标签。此外，本文提出了一种迭代训练策略来逐步优化伪标签，从而实现网络的持续改进。在两个公开数据集上的实验结果表明了我们所提方法的有效性。该方法的性能接近全监督方法，并优于多种现有的弱监督方法。