发表机构
Royal Institute for Cultural Heritage (KIK-IRPA); VIB-UGent Center for Inflammation Research（皇家文化遗产研究所; VIB-根特大学炎症研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于自监督局部描述符的块检索框架，利用少量标注在体积电子显微镜数据中定位相似细胞结构，实验证明其可靠且能显著缩减下游分析搜索空间。
AI 中文摘要
体积电子显微镜（vEM）已成为生物医学研究中一种重要的传感技术，能够以纳米级分辨率对生物细胞和组织进行三维成像。生成大规模数据集的能力已达到下游分析过程的极限，这些过程在很大程度上依赖人类专家的干预进行预处理和标注。我们提出了一种基于自监督学习的局部图像描述符的高效且可靠的基于块的检索框架，用于在vEM数据集中定位自相似结构。给定某一细胞结构的少量人工标注，我们的方法能够在整个电子显微镜体积中检索相似结构。我们的框架是交互式的，允许人类专家优化搜索查询并快速检索相关图像块，且只需少量标注数据。在真实世界生物组织的vEM图像上的实验表明，我们的框架能够可靠地识别相关细胞结构，泛化于不同细胞器及采集模态，并显著减少下游分析的搜索空间。
英文摘要
Volume electron microscopy (vEM) has emerged as an essential sensing technique in biomedical research, allowing the three-dimensional imaging of biological cells and tissues at nanometer-scale resolution. The ability to generate extensive datasets has reached the limitations of downstream analysis processes, which depend significantly on the intervention of human experts for preprocessing and annotation. We propose an efficient and reliable patch-based retrieval framework based on self-supervised learning of local image descriptors to locate self-similar structures in vEM datasets. Given a few manual annotations of a given cellular structure, our method can retrieve similar structures across the EM volume. Our framework is interactive, allowing the human expert to refine the search queries and retrieve relevant image patches quickly and using little labeled data. Experiments on real-world vEM images of biological tissues demonstrate that our framework can reliably identify relevant cellular structures, generalize across different organelles and acquisition modalities, and substantially reduce the search space for downstream analysis.
Comments41 pages, 20 figures, 5 tables. Accepted for publication in Computers in Biology and Medicine
