多焦点数字显微镜图像中化石孢粉型的可扩展检测
Scalable Detection of Fossil Palynomorphs in Multifocal Digital Microscopy Images
- Rice University（莱斯大学）
- Smithsonian National Museum of Natural History（史密森尼国家自然历史博物馆）
- Smithsonian Office of Digital and Innovation（史密森尼数字与创新办公室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出首个全玻片图像孢粉型自动检测的可扩展端到端流程，通过图像分解压缩、RF-DETR等模型基准测试、检测结果合成算法及I/O优化，将单张玻片检测时间从数天缩至1小时内，支撑大规模孢粉学研究。
AI中文摘要：
孢粉型（即花粉、孢子、甲藻等具有机壁的微体化石）是古气候的高分辨率重要记录，对古生态系统研究至关重要。现有方法依赖人工分析高分辨率多焦点数字显微镜图像，速度慢、耗时长，迫使研究人员在调查规模上做出妥协。据所知，本研究提出了首个针对全玻片图像中孢粉型自动检测的可扩展端到端流程，通过以下方式解决该瓶颈：（1）将数字化多焦点显微镜玻片图像分解并压缩为易于分析的二维图块的高效方法；（2）对包括RF-DETR在内的现代目标检测模型进行基准测试，用于孢粉型检测，达到0.879的AP@50；（3）针对大规模高分辨率图像的检测结果合成的高效算法；（4）I/O优化，实现更快的推理时间。本方法将单张玻片的孢粉型检测时间从通常需数天的人工检查大幅缩短至不到一小时的自动分析，使孢粉学研究能够在大得多的规模上开展。
英文摘要：
Palynomorphs (microscopic, organic-walled fossils such as pollen, spores, and dinoflagellates) are important high-resolution records of past climates and are critical to the study of ancient ecosystems. Existing methods rely on manual analysis of high-resolution, multifocal digital microscopy images, which is slow and time-consuming and requires researchers to compromise on the scale of their investigations. To the best of our knowledge, our work proposes the first ever scalable end-to-end pipeline for automated palynomorph detection in whole slide images that addresses this bottleneck through: (1) efficient methods for decomposing and compressing digitized multifocal microscope slide images into tractable 2-dimensional tiles for analysis; (2) benchmarking modern object detection models, including RF-DETR, for the detection of palynomorphs, achieving an AP@50 of 0.879; (3) an efficient algorithm for the synthesis of detection outputs across large-scale, high-resolution images; and (4) an I/O optimization resulting in faster inference time. Our methods drastically reduce the time required for palynomorph detection in a single slide from often days of manual inspection to under one hour of automated analysis, enabling palynological research at a substantially greater scale.
