用多保真神经网络从局部切片重建红细胞三维形态，精度超95%。
Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networks
- 融合低精度参考形状与高精度切片，通过双分支神经网络建模形态关联。
- 在至少两个正交切片条件下，三维坐标重建精度超过95%。
- 适合研究红细胞老化及疾病形态变化的定量分析，尤其适用于显微镜图像数据。
从部分观测（如显微镜图像）重建红细胞三维形态对理解红细胞衰老生理及多种红细胞疾病病理至关重要。本文提出一种多保真神经网络（MFNN）方法，将形态相似的低保真参考3D红细胞形状与高保真切片信息融合，以恢复完整表面。该模型结合卷积神经网络（低保真分支）与前馈神经网络（捕捉非线性形态相关性），并通过表面积和体积约束增强低保真分支的正则化。理论基础基于球面与3D红细胞表面间的拓扑同胚关系，训练数据由唾液酸-血影蛋白转化过程的耗散粒子动力学模拟生成。在正常与老化红细胞多种形态上的基准测试表明，当提供至少两个正交切片时，MFNN可实现超过95%的坐标重建精度。观察发现，包含棘突尖端的斜向切片能显著提升局部与全局特征重建效果，凸显特征感知采样价值。研究还评估了采样策略、形状差异与噪声影响，显示在物理约束训练下具有更强鲁棒性。结果表明，该方法可从常规显微镜图像中准确重建正常与衰老红细胞的三维形态，有助于对健康与疾病红细胞样本进行形态参数的定量分析。
原文摘要 · Abstract (English)
Reconstruction of 3D erythrocyte or red blood cell (RBC) morphology from partial observations, such as microscope images, is essential for understanding the physiology of RBC aging and the pathology of various RBC disorders. In this study, we propose a multi-fidelity neural network (MFNN) approach to fuse high-fidelity cross-sections of an RBC, with a morphologically similar low-fidelity reference 3D RBC shape to recover its full 3D surface. The MFNN predictor combines a convolutional neural network trained on low-fidelity reference RBC data with a feedforward neural network that captures nonlinear morphological correlations, and augments training with surface area and volume constraints for regularization in the low-fidelity branch. This approach is theoretically grounded by a topological homeomorphism between a sphere and 3D RBC surfaces, with training data generated by dissipative particle dynamics simulations of stomatocyte-discocyte-echinocyte transformation. Benchmarking across diverse RBC shapes observed in normal and aged populations, our results show that the MFNN predictor can reconstruct complex RBC morphologies with over 95% coordinate accuracy when provided with at least two orthogonal cross-sections. It is observed that informative oblique cross-sections intersecting spicule tips of echinocytes improve both local and global feature reconstruction, highlighting the value of feature-aware sampling. Our study further evaluates the influence of sampling strategies, shape dissimilarity, and noise, showing enhanced robustness under physically constrained training. Altogether, these results demonstrate the capability of MFNN to reconstruct the 3D shape of normal and aged RBCs from partial cross-sections as observed in conventional microscope images, which could facilitate the quantitative analysis of RBC morphological parameters in normal and disease-related RBC samples.
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。