从二维图像和细胞群体统计中学习三维生物物理细胞属性

中文摘要 AI 辅助

当参考仪器仅报告群体统计量而非单个细胞的标签时，从二维显微镜图像推断三维细胞属性是困难的。我们在此开发了一个群体监督框架，将单个二维红细胞图像映射到潜在的生物物理量，并将其聚合为平均红细胞体积、红细胞分布宽度和平均红细胞血红蛋白。该模型结合了共享的局部推断、用于体积和血红蛋白的生物物理结构化解码器、学习到的实例权重以及设备特定的校准。我们形式化了聚合观测识别受限实例预测器的条件，说明了为什么群体一致性本身不能识别单细胞属性或三维几何结构，并推导了子集均值匹配引起的离散度惩罚。开发数据集包含390个样本和跨六台设备的1105次采集，与Sysmex分析仪相比报告的皮尔逊相关系数为0.86--0.98。该框架提供了一条从二维图像和群体监督到三维细胞生物物理学的可测试路径，而不声称显式的三维重建。

英文摘要

Inferring 3D cellular properties from 2D microscopy is difficult when a reference instrument reports only population statistics rather than labels for individual cells. Here we develop a population-supervised framework that maps single 2D red-cell images to latent biophysical quantities and aggregates them to mean corpuscular volume, red-cell distribution width and mean corpuscular haemoglobin. The model combines shared local inference, a biophysically structured decoder for volume and haemoglobin, learned instance weighting and device-specific calibration. We formalise conditions under which aggregate observations identify restricted instance predictors, show why population agreement does not by itself identify single-cell properties or 3D geometry, and derive the dispersion penalty induced by subset mean matching. The development dataset comprises 390 specimens and 1,105 acquisitions across six devices, with reported Pearson correlations of 0.86--0.98 against a Sysmex analyser. The framework provides a testable route from 2D images and population supervision to 3D cellular biophysics without claiming explicit 3D reconstruction.

发表机构

• Algocyte | Oxford Immune Algorithmics（Algocyte | 牛津免疫算法公司）
• Oxford University Innovation（牛津大学创新公司）
• London Institute for Healthcare Engineering（伦敦医疗工程研究所）

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