用深度学习自动数病毒斑，省时又准。
End-to-end plaque counting and virus titration from laboratory plate images with deep learning
- 基于SAM模型分两步：先定位培养孔，再识别病毒斑。
- 在三种病毒数据上相关性超0.88，与人工标注高度一致。
- 适合病毒实验室做高通量、可复现的感染滴度分析。
斑点实验仍是衡量病毒感染力的金标准，但人工数斑点耗时且易因人而异。本文提出一个端到端的计算机辅助流程，直接从实验室斑点实验图像中完成细胞病变效应型病毒滴度测定。该方法结合两个源自分割任意模型（SAM）的模块：基于SAM2的孔定位模块，可在不同成像条件下准确识别培养孔；以及基于SAM的斑点分割模型，用于检测并统计每个孔内的斑点数量。评估使用包含私人数据（玛雅病毒和柯萨奇病毒B3）及公开数据（VACVPlaque数据集中的痘苗病毒）的混合数据集。系统输出每孔斑点数，自动计算斑形成单位/毫升（PFU/mL），并集成于网页平台，支持结果审核与实验管理。在独立测试板上（玛雅/柯萨奇病毒17张，痘苗病毒22张），系统在6孔和12孔板格式下均表现良好，与人工标注的相关系数分别为0.92（玛雅/柯萨奇病毒）和0.88（痘苗病毒）。自动化计数与四位独立专家标注高度一致。该系统将在论文接受后开源，推动可复现、可审计、高效的大规模斑点实验分析。
原文摘要 · Abstract (English)
Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We present an end-to-end, computer-aided workflow for cytopathic effect-based virus titration directly from laboratory plaque assay images. The proposed approach combines two models derived from the Segment Anything Model (SAM): a SAM2-based well-segmentation module that localizes assay wells across heterogeneous imaging conditions, and a SAM-based plaque-segmentation model that detects and enumerates plaques within each well. The method was evaluated on a mixed dataset comprising private plaque assay images of Mayaro virus and Coxsackievirus B3, together with public Vaccinia virus images from the VACVPlaque dataset. The pipeline outputs per-well plaque counts, automatically computes plaque-forming units per milliliter (PFU/mL), and is integrated into a web-based platform that allows users to review results and organize experiments. On held-out plates (17 from MAYV/CVB3 and 22 from VACV), the workflow generalized across two plate formats (6-well and 12-well) and showed strong agreement with manual annotations (Pearson correlation coefficients of 0.92 for MAYV/CVB3 and 0.88 for VACV). Automated plaque counts were further compared with annotations from four independent experts, demonstrating high concordance. The proposed system will be open sourced and publicly released upon acceptance of this manuscript to enable reproducible, scalable, and audit-ready plaque assay analysis while substantially reducing manual annotation effort.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。