Prompt-DAS: Annotation-Efficient Prompt Learning for Domain Adaptive Semantic Segmentation of Electron Microscopy Images

2025/09/23 by Jiabao Chen, Chen, Jiabao, Shan Xiong +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Electron Microscopy Techniques and Applications #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2509.18973

openalex publication_date 2025/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
• Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
• Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
• SAM-Med2D
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
• Micrograph segmentations for DDEVD
• Segment Anything
• WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy
• DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
• Emerging Properties in Self-Supervised Vision Transformers
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Panoptic Segmentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation

Related

• Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
• WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy
• Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
• SAMDA: Leveraging SAM on Few-Shot Domain Adaptation for Electronic Microscopy Segmentation
• Domain Adaptive Segmentation in Volume Electron Microscopy Imaging