Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling

TL;DR

This paper presents Spotlight, a novel virtual staining method that emphasizes cellular structures in 3D microscopy images, improving morphological accuracy and downstream analysis performance.

Contribution

Spotlight introduces histogram-based foreground masking and shape-aware loss functions for more accurate 3D virtual staining of cellular images.

Findings

Enhanced morphological representation in 3D virtual stains

Improved downstream segmentation and profiling accuracy

Better focus on relevant cellular structures

Abstract

Microscopy enables direct observation of cellular morphology in 3D, with transmitted-light methods offering low-cost, minimally invasive imaging and fluorescence microscopy providing specificity and contrast. Virtual staining combines these strengths by using machine learning to predict fluorescence images from label-free inputs. However, training of existing methods typically relies on loss functions that treat all pixels equally, thus reproducing background noise and artifacts instead of focusing on biologically meaningful signals. We introduce Spotlight, a simple yet powerful virtual staining approach that guides the model to focus on relevant cellular structures. Spotlight uses histogram-based foreground estimation to mask pixel-wise loss and to calculate a Dice loss on soft-thresholded predictions for shape-aware learning. Applied to a 3D benchmark dataset, Spotlight improves morphological representation while preserving pixel-level accuracy, resulting in virtual stains better suited for downstream tasks such as segmentation and profiling.