Abstract
Domain adaptive segmentation (DAS) of numerous organelle instances from large-scale electron microscopy (EM) is a promising way to enable annotation-efficient learning. Inspired by SAM, we propose a promptable multitask framework, namely Prompt-DAS, which is flexible enough to utilize any number of point prompts during the adaptation training stage and testing stage. Thus, with varying prompt configurations, Prompt-DAS can perform unsupervised domain adaptation (UDA) and weakly supervised domain adaptation (WDA), as well as interactive segmentation during testing. Unlike the foundation model SAM, which necessitates a prompt for each individual object instance, Prompt-DAS is only trained on a small dataset and can utilize full points on all instances, sparse points on partial instances, or even no points at all, facilitated by the incorporation of an auxiliary center-point detection task. Moreover, a novel prompt-guided contrastive learning is proposed to enhance discriminative feature learning. Comprehensive experiments conducted on challenging benchmarks demonstrate the effectiveness of the proposed approach over existing UDA, WDA, and SAM-based approaches.
Abstract (translated)
领域自适应分割（DAS）从大规模电子显微镜（EM）图像中识别众多细胞器实例是一种有望提高注释效率的学习方式。受到SAM的启发，我们提出了一种可提示的多任务框架——Prompt-DAS，它在适应性训练阶段和测试阶段都能够灵活地利用任意数量的点提示。因此，通过不同的提示配置，Prompt-DAS可以执行无监督领域自适应（UDA）、弱监督领域自适应（WDA）以及交互式分割。与基础模型SAM不同，后者需要为每个单独的对象实例提供一个提示，Prompt-DAS仅在一个小数据集上进行训练，并且能够利用所有实例的全部点、部分实例的稀疏点或根本不使用任何点，这得益于辅助中心点检测任务的加入。此外，还提出了一种新颖的提示引导对比学习方法以增强判别性特征的学习。在具有挑战性的基准上的全面实验表明，所提出的方法优于现有的UDA、WDA和基于SAM的方法。
URL
https://arxiv.org/abs/2509.18973
