Traditional fluorescence staining is phototoxic to live cells, slow, and expensive; thus, the subcellular structure prediction (SSP) from transmitted light (TL) images is emerging as a label-free, faster, low-cost alternative. However, existing approaches utilize 3D networks for one-to-one voxel level dense prediction, which necessitates a frequent and time-consuming Z-axis imaging process. Moreover, 3D convolutions inevitably lead to significant computation and GPU memory overhead. Therefore, we propose an efficient framework, SparseSSP, predicting fluorescent intensities within the target voxel grid in an efficient paradigm instead of relying entirely on 3D topologies. In particular, SparseSSP makes two pivotal improvements to prior works. First, SparseSSP introduces a one-to-many voxel mapping paradigm, which permits the sparse TL slices to reconstruct the subcellular structure. Secondly, we propose a hybrid dimensions topology, which folds the Z-axis information into channel features, enabling the 2D network layers to tackle SSP under low computational cost. We conduct extensive experiments to validate the effectiveness and advantages of SparseSSP on diverse sparse imaging ratios, and our approach achieves a leading performance compared to pure 3D topologies. SparseSSP reduces imaging frequencies compared to previous dense-view SSP (i.e., the number of imaging is reduced up to 87.5% at most), which is significant in visualizing rapid biological dynamics on low-cost devices and samples.

翻译：传统荧光染色对活细胞具有光毒性、速度慢且成本高昂；因此，从透射光图像预测亚细胞结构正成为一种无标记、更快速、低成本的替代方案。然而，现有方法采用三维网络进行一对一体素级密集预测，这需要频繁且耗时的Z轴成像过程。此外，三维卷积不可避免地导致显著的计算和GPU内存开销。为此，我们提出一种高效框架SparseSSP，该框架以高效范式预测目标体素网格内的荧光强度，而非完全依赖三维拓扑结构。具体而言，SparseSSP对先前研究作出两项关键改进：首先，引入一对多体素映射范式，允许稀疏透射光切片重建亚细胞结构；其次，提出混合维度拓扑结构，将Z轴信息折叠至通道特征中，使二维网络层能够以较低计算成本处理亚细胞结构预测任务。我们通过大量实验验证了SparseSSP在不同稀疏成像比例下的有效性和优势，与纯三维拓扑相比，本方法取得了领先性能。相较于先前的密集视图亚细胞结构预测方法，SparseSSP显著降低了成像频率（成像次数最多可减少87.5%），这对于在低成本设备和样本上可视化快速生物动力学过程具有重要意义。