Tomocube

1. Pipeline: (3D) FL 1Ch Segmentation & HT Quantification per Cell AI-Powered

This pipeline builds upon the FL Segmentation & HT Quantification workflow by incorporating 3D cell segmentation, enabling per-cell analysis. Fluorescence signals are used to segment target structures, and a separate 3D cell mask is generated to assign each object to its corresponding cell.

Using these masks, the pipeline performs quantitative analysis within individual cells by combining fluorescence-based segmentation with HT-based measurements such as refractive index, volume, and dry mass. This allows precise, single-cell level analysis of labeled structures in 3D holotomographic data.

Output: 3D object and cell masks with instance labels, Object-to-cell assignments, Per-cell fluorescence and HT-based measurements
 

This pipeline enables per-cell analysis in 2D by combining fluorescence-based object segmentation with AI-powered cell segmentation. A single fluorescence channel is used to segment target objects, and Cellpose is applied to generate 2D cell masks. Each object is then assigned to a corresponding cell based on spatial overlap.

Using these masks, the pipeline provides both fluorescence-based and holotomography-based measurements at the single-cell level, including area, intensity, refractive index, and dry mass.

Output: Object and cell masks with instance labels, Per-cell fluorescence-based and HT-based measurements





3. Pipeline: (3D) FL 1Ch Segmentation & FL Quantification per Cell AI-Powered

This pipeline enables per-cell analysis of fluorescence-labeled structures in 3D by combining fluorescence-based object segmentation with AI-powered 3D cell segmentation. Target objects are segmented using fluorescence intensity, and each object is assigned to its corresponding cell based on spatial overlap.

Quantitative measurements, including object count, volume, and fluorescence intensity, are calculated on a per-cell basis, allowing detailed analysis of labeled structures within individual cells across the 3D field of view.

Output: Object and cell masks with instance labels, Object-to-cell assignments, Per-cell fluorescence-based measurements
 

4. Pipeline: (2D) FL 1Ch Segmentation & FL Quantification per Cell AI-Powered

This pipeline enables per-cell fluorescence analysis in 2D by combining object segmentation with AI-powered cell segmentation. A fluorescence channel is used to segment target objects, and Cellpose is applied to generate 2D cell masks. Each object is then assigned to its corresponding cell based on spatial overlap.

Fluorescence-based measurements such as object count, area, and intensity are calculated on a per-cell basis, enabling detailed analysis of labeled structures within individual cells.

Output: Object and cell masks with instance labels, Object-to-cell assignments, Per-cell fluorescence-based measurements




Access to analysis pipelines is available upon request. Please send your inquiry to info@tomocube.com to explore the options best suited to your needs.