Guided Augmentation for Monocular Depth Estimation in Cell Microscopy

Monocular Depth Estimation (MDE) in cell microscopy provides critical insights into cellular structures, with applications spanning cancer diagnostics, hematological analysis, and tumor margin assessment. However, it presents unique challenges such as sparse z-stacks with limited focal planes, optical aberrations degrading depth precision, and the inherently ill-posed nature of inferring depth from single 2D images. Existing MDE methods often rely on semantic priors, geometric modeling, or self-supervised learning. While effective in macroscopic applications, these approaches struggle with microscopy-specific challenges involving domain-specific feature distributions.