Daniele Pirone

This paper introduces Biolens Phase Compensation to enable 3D nucleoli measurement in HTFC, achieving intranuclear specificity comparable to fluorescence microscopy.

This paper evaluates image representations in holographic imaging flow cytometry to balance classification accuracy and computational efficiency for deep learning-based cell type classification.

This paper introduces HTFC, a label-free flow cytometry method for X-ray dose-response profiling, showing high correlation with clonogenic assays and faster results.

The paper introduces a pipeline to profile co-occurring morphological phenotypes in vacuolated cells using HTFC and fractal analysis, assigning severity levels to each phenotype.

This paper introduces a Holotomography-driven learning method for virtual staining in flow cytometry without requiring co-registration, enabling label-free single-cell analysis.

This paper introduces a self-consistent method for automatically retrieving single cell rotations in holo-tomographic flow cytometry, achieving full automation and superior accuracy.

This paper introduces HTFC to quantify lysosomal accumulation in LSDs, identifies novel biomarkers, and links 3D lysosomal structure to therapeutic efficacy.

This paper introduces a method to simulate 3D refractive index tomograms of suspended cells and provides a benchmark dataset for algorithm development.