Abstract
Automatic medical image segmentation is a fundamental step in computer-aided diagnosis, yet fully supervised approaches demand extensive pixel-level annotations that are costly and time-consuming. To alleviate this burden, we propose a weakly supervised segmentation framework that leverages only four extreme points as annotation. Specifically, bounding boxes derived from the extreme points are used as prompts for the Segment Anything Model 2 (SAM2) to generate reliable initial pseudo labels. These pseudo labels are progressively refined by an enhanced Feature-Guided Extreme Point Masking (FGEPM) algorithm, which incorporates Monte Carlo dropout-based uncertainty estimation to construct a unified gradient uncertainty cost map for boundary tracing. Furthermore, a dual-branch Uncertainty-aware Scale Consistency (USC) loss and a box alignment loss are introduced to ensure spatial consistency and precise boundary alignment during training. Extensive experiments on two public ultrasound datasets, BUSI and UNS, demonstrate that our method achieves performance comparable to, and even surpassing fully supervised counterparts while significantly reducing annotation cost. These results validate the effectiveness and practicality of the proposed weakly supervised framework for ultrasound image segmentation.
Abstract (translated)
自动医学图像分割是计算机辅助诊断中的一个基本步骤，但完全监督的方法需要大量的像素级注释，这些注释成本高昂且耗时。为了减轻这一负担，我们提出了一种弱监督分割框架，该框架仅使用四个极端点作为标注。具体而言，从这四个极端点衍生出的边界框被用作提示，以引导Segment Anything Model 2 (SAM2)生成可靠的初始伪标签。随后，通过增强型特征指导极点掩膜（Feature-Guided Extreme Point Masking, FGEPM）算法对这些伪标签进行逐步优化，并结合基于蒙特卡洛丢弃法的不确定性估计来构建统一的梯度不确定性成本图以追踪边界。 此外，我们还引入了一种双分支不确定性感知尺度一致性损失 (Uncertainty-aware Scale Consistency, USC) 以及一个框对齐损失，确保在训练过程中空间一致性和精确边界对齐。在两个公开的超声数据集（BUSI和UNS）上的广泛实验表明，我们的方法在性能上与完全监督的方法相当甚至超越它们的同时显著降低了注释成本。这些结果验证了我们提出的弱监督框架对于超声图像分割的有效性和实用性。
URL
https://arxiv.org/abs/2510.15666
