SparseSSP: 3D Subcellular Structure Prediction from Sparse-View Transmitted Light Images

TL;DR

SparseSSP offers a low-cost, efficient method for 3D subcellular structure prediction from transmitted light images by using sparse slices and hybrid 2D/3D topology, reducing imaging frequency significantly.

Contribution

It introduces a novel sparse voxel mapping and hybrid topology that enable efficient 3D structure prediction with reduced computational cost and imaging frequency.

Findings

Achieves leading performance compared to pure 3D methods.

Reduces imaging frequency by up to 87.5%.

Validates effectiveness across diverse sparse imaging ratios.

Abstract

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.