用极点追踪实现超声图像弱监督分割，效果接近全监督方法。
DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation
- 基于特征图代价矩阵，通过最低成本路径连接极点生成伪标签。
- 迭代训练策略逐步优化伪标签，实现模型持续提升。
- 适合标注困难的医学图像分割任务，尤其适用于超声影像。
自动医学图像分割在计算机辅助诊断中至关重要。然而，全监督学习方法通常需要大量耗时的人工标注。为应对这一挑战，弱监督学习方法，特别是利用极点作为监督信号的方法，具有潜在优势。本文提出一种结合特征引导极点掩码（FGEPM）的深度极点追踪（DEPT）算法，用于超声图像分割。该方法通过在基于特征图的代价矩阵中寻找连接所有极点的最低成本路径，生成伪标签。同时，提出一种迭代训练策略，逐步优化伪标签，实现网络性能的持续提升。在两个公开数据集上的实验结果表明，该方法性能接近全监督方法，并优于多种现有弱监督方法。
原文摘要 · Abstract (English)
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.
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