FIJI_Macros

This repository contains a collection of ImageJ/Fiji macros for quantitative image analysis in biomedical research. The macros are designed for a variety of applications. Each macro is tailored for specific workflows and imaging modalities.

There are 99 macros described in this document.

Summary Table:

Prefix	Meaning/Modality
All_	General/batch/preprocessing macros
BioVoxxel_	Advanced 2D/3D analysis tools
CT_	Computed Tomography (CT)
EM_	Electron Microscopy
IF_	Immunofluorescence (2D)
IF2Dtime_	Immunofluorescence (2D time-lapse)
IF3D_	Immunofluorescence (3D stacks)
IF4D_	Immunofluorescence (4D: 3D + time)
PHC_	Phase Contrast Microscopy
PHC3D_	Phase Contrast (3D stacks)
WF_	Widefield Microscopy
WSI_	Whole Slide Imaging (histology slides)

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Platform Overview

Institution: CIMA – Center for Applied Medical Research, University of Navarra (cima.cun.es) Service: Imaging Platform – Software Development Unit Contact: Tomás Muñoz Santoro (tmsantoro@unav.es)

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Image Modalities

Modality	Prefix / Files	Details
Immunofluorescence 2D	IF_	Confocal / epifluorescence, CZI & TIF
Immunofluorescence 2D time-lapse	IF2Dtime_	Time series, single plane
Immunofluorescence 3D	IF3D_	Z-stack confocal, volumetric analysis
Immunofluorescence 4D	IF4D_	Live time-lapse + Z-stack
Whole Slide Imaging	WSI_	SVS digital pathology scanner
Phase Contrast 2D	PHC_	Label-free transmitted light
Phase Contrast 3D	PHC3D_	Z-stack phase contrast (organoids)
Widefield fluorescence	WF_	Standard epifluorescence
Micro-CT	CT_	3D computed tomography volumes
Electron Microscopy	EM_	TEM ultrastructure (liposomes)
FISH / RNA ISH	IF_FISH, old/PipelineFISH, old/IF_ISH_HDV_F480	Fluorescence in situ hybridization and RNA chromogenic ISH
Serial section IHC with registration	IF_GFP_GP100	SIFT-aligned serial H&E + DAB sections
Multispectral	IF_CellClassPhenotype_Vectra	Vectra/Polaris multispectral unmixing

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Tissue and Sample Types

Organs and Tissues

Organ	Key Macros
Brain	WSI_BrownDetectionArea_Brain*, WSI_BrainTumor*, WSI_Brain_SynNM, IF3D_MicrogliaMarkerColoc, IF3D_NeuronFiberColoc, IF3D_NeuronMicrogliaProteinColoc, IF3D_NeuronPhagocytosis, IF_Neuron, IF_Soma_and_axon_size_2D, IF_AggregatesCount
Heart / Cardiac	WSI_BrownDetectionArea_Heart, WSI_SiriusRedCardio, WSI_SiriusRedPerivascular, WSI_Desmin, IF_CardioFibroblast, IF_CardiomyocytesCount, IF_CardioAngiogenesis, IF_CardioDeath, IF_CardioHypertrophy, IF3D_SarcomereAnalysis, IF_manualCardio_v2, IF_LamininCSA_v2
Liver	WSI_BrownDetectionArea_Liver, WSI_Steatosis, WSI_SiriusRed*, Adipophilin, old/IF_ISH_HDV_F480, CT_LiverTumors
Lung	WSI_BrownDetectionArea_Lung, WSI_LungNodulesHE, WSI_LungTissueCD3, CT_LungVolume_Auto
Muscle	WSI_BrownDetectionArea_Muscle, WSI_Desmin, IF_LamininCSA_v2, old/SiriusRed_v2.0_Muscle
Spleen	WSI_BrownDetectionArea_Spleen
Retina	IF3D_RetinVasculature, RetinVasculature3D
Aorta	IF_AorticRings, AorticRings_ManualRing
Bone	CT_BoneAnalysis_FemurMonetite, CT_BoneFat_PrePost, CT_tibiaCartige, CT_Manguito, WSI_TRAP, WSI_MMPsafranin, WSI_CartilageToulinaBlue
Rotator cuff	CT_Manguito, IF3D_ManguitoRotador
Brown / White Adipose	WSI_BrownFatSize, Adiposoft17, CT_BoneFat_PrePost
Lymph nodes	WSI_LymphTcount

In Vitro / Cell Models

Model	Key Macros
Organoids (3D)	PHC3D_OrganoidCount
Transwell migration / invasion	WSI_PAS_Transwell, WSI_TranswellCells
Aortic ring angiogenesis	IF_AorticRings, AorticRings_ManualRing
Monocytes (brightfield)	PHC_MonocyteCount
Biofilms	IF3D_BiofilmQuantification, IF3D_BiofilmQuantification_Cocultivos, IF3D_BiofilmQuantification_LiveDead, IF4D_BiofilmQuantification, IF_BiofilmLiveDead
Bacteria	IF_Bacteria
Liposomes	EM_Liposoms

Circulating / Rare Cells

Cell type	Key Macros
Circulating Tumor Cells (CTCs)	IF_CellClass_CTCphenotype
Neutrophils / NETs	IF_NETs, IF_Neutrophils_N2
Platelets	IF_Vessels_Platelets

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Staining and Labeling Techniques

Technique	Description	Key Macros
Immunofluorescence (multichannel, DAPI)	Standard fluorescent markers	Most IF_* files
DAB chromogenic IHC (brown)	Peroxidase-based brown detection	WSI_BrownDetectionArea_*, WSI_DABintensity_ROIs, WSI_CapillaryDAB
H&E (hematoxylin & eosin)	Standard histology stain	WSI_NecrosisHE, WSI_LungNodulesHE, IF_GFP_GP100
Sirius Red (collagen / fibrosis)	Fibrillar collagen detection	WSI_SiriusRed* (5 variants)
Safranin-O (cartilage GAGs)	Proteoglycan detection	WSI_MMPsafranin
Toluidine Blue (cartilage)	Proteoglycan / ECM staining	WSI_CartilageToulinaBlue
PAS (glycoproteins / glycogen)	Polysaccharide detection	WSI_PAS_Transwell
TRAP (osteoclasts)	Tartrate-resistant acid phosphatase	WSI_TRAP
Eosin intensity	Cytoplasmic stain intensity	WSI_EosinIntensity
FISH	Fluorescence in situ hybridization	IF_FISH, old/PipelineFISH_Aqua
RNA ISH	Chromogenic RNA detection (HDV)	old/IF_ISH_HDV_F480
GFP / fluorescent reporters	Live/fixed reporter gene imaging	IF_GFP_GP100
Live/Dead viability	Cell viability discrimination	IF_CellClassLifeDead, IF_CellLifeDead_ChannelDispo, IF3D_BiofilmQuantification_LiveDead
Osmium (bone marrow fat, CT)	Lipid staining for microCT	CT_BoneFat_PrePost
Adipophilin (lipid droplets)	Lipid droplet surface marker	Adipophilin
Multispectral unmixing	Simultaneous multi-marker separation	IF_CellClassPhenotype_Vectra
Colour deconvolution H&E DAB	Chromogen separation	IF_GFP_GP100, WSI_*

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Research Domains and Biological Targets

Domain	Specific Targets	Key Macros
Neuroscience / Neuropathology	Microglia (IBA1), neurons, myelin (MBP, SMI38), neuromelanin, synuclein, axon/soma size, phagocytosis, neuron–microglia interaction, protein aggregates	IF3D_Microglia*, IF3D_Neuron*, WSI_Brain*
Cardiology	Cardiomyocytes, fibroblasts, sarcomeres, hypertrophy, cell death, angiogenesis, fibrosis (Sirius Red), Desmin, Laminin	IF_Cardio*, IF3D_SarcomereAnalysis, WSI_SiriusRedCardio*
Oncology / Tumor biology	GFP+/GP100+ (melanoma), CTCs, brain tumors (IBA1, CD markers), lung nodules, necrosis, immune infiltration	IF_GFP_GP100, IF_CellClass_CTC*, WSI_BrainTumor*, WSI_LungNodulesHE
Hepatology	Steatosis, lipid droplets (adipophilin), fibrosis (Sirius Red), HDV RNA in macrophages, liver tumors	WSI_Steatosis, Adipophilin, old/IF_ISH_HDV_F480, CT_LiverTumors
Immunology / Inflammation	CD3 T-cells, CD45 leukocytes, CD markers (generic), IBA1 microglia/macrophages, M1/M2 macrophage polarization, neutrophils N2, NETs (NETosis), thrombus macrophage phenotype, lymphocytes T	WSI_CD*, IF_MacrophageClass, IF_NETs, IF_TrombosM1M2
Orthopedics / Biomaterials	Femur (Monetite implant / bone ingrowth), tibia cartilage thickness, rotator cuff, TRAP osteoclasts, cartilage GAGs	CT_BoneAnalysis_FemurMonetite, CT_Manguito, WSI_TRAP, WSI_MMPsafranin
Vascular biology	Angiogenesis (aortic rings, capillaries), vessel tortuosity, retinal vasculature, platelets, fibrin mesh	IF_Angiogenesis, IF3D_VesselsTortousity, IF_FibrinMesh*, IF_Vessels_Platelets
Microbiology / Infection	Biofilm (live/dead, co-cultures, 4D live), bacteria quantification, live infection (4D)	IF3D_Biofilm*, IF4D_*, IF_Bacteria
DNA damage / Genotoxicity	DNA damage foci (Rhodamine), nuclear foci in tissue	IF_FociClass, IF_FociTissue
Cell stress / Molecular biology	ATF4 (ER stress / UPR), protein aggregates, centrosomes, nuclear protein colocalization, nucleolus, nuclear dots	IF_ATF4, IF_AggregatesCount, IF3D_Centriolos, IF_Nucleolos*, IF3D_NuclearDots
Adipose biology / Metabolism	White adipose cellularity, brown fat size, bone marrow fat (osmium microCT)	Adiposoft17, WSI_BrownFatSize, CT_BoneFat_PrePost
Nanomedicine	Liposome ultrastructure (TEM), nanoparticle tracking in live cells	EM_Liposoms, IF_NanoTrack
Pulmonology	Lung nodules (H&E), CD3 infiltration in lung, lung volume (CT)	WSI_LungNodulesHE, WSI_LungTissueCD3, CT_LungVolume_Auto
Ophthalmology	3D retinal vasculature reconstruction and quantification	IF3D_RetinVasculature, RetinVasculature3D
Environmental / Toxicology	Microplastic detection in tissue	IF_TissueMicroPlastics

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Adiposoft17.ijm / old/Adiposoft16.ijm

• Purpose: Automated and manual quantification of adipocyte size and number in microscopy images.
• Features: Batch processing, edge exclusion, calibration in microns or pixels, manual ROI editing.
• Output: Results as .xls and .csv, annotated images.

Adipophilin.ijm

• Purpose: Quantifies adipophilin-positive areas in tissue sections.
• Features: Manual ROI selection, tissue/adipophilin thresholding, artifact removal.
• Output: Quantification table and annotated images.

All_exploreFeatures.ijm

• Purpose: Extracts and summarizes image features (bit depth, intensity, Otsu stats) for batches.
• Features: Batch processing, Excel export.
• Output: .xls report of image features.

All_ReduceDimensionality.ijm

• Purpose: Reduces dimensionality of multi-dimensional images (channels, slices, frames).
• Features: Batch and single-file mode, flexible dimension selection.
• Output: Split images per dimension.

All_removeArtefacts.ijm

• Purpose: Removes unwanted regions/artifacts from images by manual selection.
• Features: Interactive background selection and artifact removal.
• Output: Preprocessed images.

All_RotateReslice.ijm

• Purpose: Rotates and reslices CT images for orientation correction.
• Features: User-defined rotation angle, batch and single-file mode.
• Output: Rotated/resliced images.

All_SplitFields.ijm

• Purpose: Splits multi-field images (e.g., confocal) into individual fields.
• Features: User-defined grid (columns/rows), batch and single-file mode.
• Output: Individual field images.

All_SplitSamples.ijm

• Purpose: Splits multi-sample images (e.g., WSI) into single-sample images.
• Features: User-defined number of samples, batch and single-file mode.
• Output: Cropped images per sample.

All_SVSchangeFormat.ijm

• Purpose: Converts Aperio SVS and other formats to TIFF/HDF5.
• Features: Batch and single-file mode, supports various input formats.
• Output: Converted images in new format.

BioVoxxel_Toolbox.ijm

• Purpose: Advanced toolbox for 2D/3D image analysis, including particle analysis, shape descriptors, and feature extraction.
• Features: Analyze particles by shape, edge correction, field of view count, threshold checks, background correction, and more.
• Output: Quantitative results, processed images, and overlays for further analysis.

BioVoxxel_3D_Box.ijm

• Purpose: Suite of 3D image processing tools for isotropic correction, filtering, and label analysis.
• Features: Voxel and image isotropy, flat field correction, Difference of Gaussian, recursive filtering, threshold labeling, label splitting, object inspection, and neighbor analysis.
• Output: Enhanced and processed 3D images, label maps, and analysis results.

CT_BoneAnalysis_FemurMonetite.ijm

• Purpose: Analyze bone and scaffold in femur CT images, including volume and intensity quantification.
• Features: Prism selection, manual annotation of implants, interpolation, and quantification inside/outside scaffold.
• Output: Volume and intensity measurements, annotated images, and results tables.

CT_BoneFat_PrePost.ijm

• Purpose: Quantify bone marrow fat using pre- and post-decalcification CT images.
• Features: Segments bone and fat, measures volumes, calculates fat ratio, and supports user-defined thresholds.
• Output: Fat and bone volume ratios, results tables, and processed images.

CT_LiverTumors.ijm

• Purpose: Semiautomatic segmentation and quantification of 3D tumors in microCT images.
• Features: Automatic/manual annotation, tumor volume and diameter calculation, batch processing.
• Output: Tumor volume and size data, annotated images, and Excel/CSV reports.

CT_Manguito.ijm

• Purpose: Analyze inflammation and fat in CT images of the rotator cuff.
• Features: Manual region selection, automatic detection of inflammation and fat, peripheral ring quantification.
• Output: Quantitative results for inflammation and fat, annotated images, and summary tables.

CT_removeRingArtefactsFFT.ijm

• Purpose: Remove ring artifacts from microCT images using FFT-based filtering.
• Features: Batch processing of DICOM folders, FFT filtering, and automated artifact removal.
• Output: Preprocessed images with reduced artifacts, ready for further analysis.

CT_tibiaCartige.ijm

• Purpose: Automatic segmentation and quantification of tibia cartilage in microCT images.
• Features: Cartilage segmentation, thickness mapping, condyle ROI analysis, and profile plotting.
• Output: Cartilage thickness maps, quantitative tables, and annotated images.

Drawing Tools.txt

• Purpose: Custom drawing tools for manual annotation and editing in ImageJ.
• Features: Pencil, paintbrush, eraser, spray can, flood fill, and arrow tools with adjustable parameters.
• Output: Manually annotated or edited images.

EM_Liposoms.ijm

• Purpose: Semiautomatic segmentation and quantification of liposomes in electron microscopy images.
• Features: Annotation of single, multilamellar, and inner liposomes, ellipse fitting, ROI management, and size feature extraction.
• Output: Annotated ROI sets, summary statistics, and Excel reports.

IF_ATF4.ijm

• Purpose: Automatic classification of ATF4+/- cells in confocal IF images (DAPI + ATF4).
• Features: Nuclei segmentation, marker-controlled watershed, quantification of ATF4+ cells.
• Output: QuantificationResults.xls, annotated images.

IF_Angiogenesis.ijm

• Purpose: Quantification of angiogenesis (vessel area and number) in IF images.
• Features: Manual ROI selection, tissue/vessel segmentation, vessel counting.
• Output: Quantification_angiogenesis.xls, annotated images.

IF_AorticRings.ijm

• Purpose: Quantifies aortic ring and branches in 2D/3D images.
• Features: Automatic detection, manual editing, skeleton analysis for branch length/number.
• Output: _QuantifiedBranches.xls, tagged skeleton images.

IF_Bacteria.ijm

• Purpose: Quantifies bacteria in IF images (DAPI, red, green).
• Features: Segmentation, marker-controlled watershed, intensity filtering.
• Output: Bacteria_results_individual.xls, Bacteria_results_averages.xls, annotated images.

IF_BiofilmLiveDead.ijm

• Purpose: Quantifies live/dead bacteria in biofilm images.
• Features: Thresholding, area calculation, live/dead ratio.
• Output: QuantificationResults_IF_BiofilmLiveDead.xls, overlay images.

IF_CardioFibroblast.ijm

• Purpose: Counts fibroblasts in cardiac tissue based on double phenotype (SMA+, CAV-).
• Features: Tissue/ROI selection, marker segmentation, vessel exclusion.
• Output: QuantificationResults_IF_FibroblastCount.xls.

IF_CardiomyocytesCount.ijm

• Purpose: Automatic detection and quantification of cardiomyocytes.
• Features: Tissue/membrane segmentation, manual editing, area measurement.
• Output: Quantification_Cardiomyocytes.xls, SegmentationResults.xls, annotated images.

IF_Centriolos.ijm

• Purpose: Quantifies centrioles and their distances to nuclei in IF images.
• Features: 3D stack processing, nuclei/centriole segmentation, distance calculation.
• Output: QuantificationResults_IF_Centriolos.xls.

IF_CellClassPhenotype.ijm

• Purpose: Automatic classification of cell phenotypes (e.g., DAPI + marker).
• Features: Nuclei segmentation, marker-controlled watershed, phenotype quantification.
• Output: Quantification_[phName]CellPhenotype.xls, annotated images.

IF_CellClassDoublePhenotype.ijm

• Purpose: Classifies cells based on single/double phenotypes (e.g., DAPI + 3 markers).
• Features: Tissue/ROI segmentation, marker segmentation, phenotype counting.
• Output: QuantificationResults.xls.

IF_CellClassLifeDead.ijm

• Purpose: Classifies cells as live/dead based on phenotype markers.
• Features: DAPI segmentation, marker segmentation, phenotype quantification.
• Output: Quantification_LifeDead.xls.

IF_CellCount.ijm

• Purpose: Counts nuclei in 2D confocal images.
• Features: Channel selection, size filtering, batch mode.
• Output: Quantification_IF2D_CellCount.xls, annotated images.

IF_DAPI_Green_inNuclei.ijm

• Purpose: Quantifies DAPI and marker areas, computes DAPI/marker ratio.
• Features: Channel selection, background correction, thresholding, area measurement.
• Output: IF_quantification.xls, annotated images.

IF_DAPI_Green_perNucleus.ijm

• Purpose: Quantifies marker intensity within each nucleus.
• Features: Nuclei segmentation, per-nucleus measurement.
• Output: IF_quantification_[image].xls, annotated images.

IF_FibrinMesh3D.ijm

• Purpose: 3D analysis of fibrin mesh in confocal images.
• Features: 3D segmentation, BoneJ analysis, thickness/length quantification.
• Output: Quantification_FibrinMesh.xls, segmentation images.

IF_FISH.ijm

• Purpose: Quantifies FISH signals (red/green) in multi-channel images.
• Features: Channel selection, background correction, manual ROI, artifact removal.
• Output: Processed images, quantification tables.

IF_FociClass.ijm

• Purpose: Automatic detection and classification of DNA damage foci.
• Features: Nuclei segmentation, marker-controlled watershed, phenotype quantification.
• Output: Quantification tables, annotated images.

IF_FociTissue.ijm

• Purpose: Quantifies foci (e.g., DNA damage) in tissue.
• Features: Tissue/nuclei segmentation, foci detection, area/intensity measurement.
• Output: Foci_results.xls, annotated images.

IF_GFP_GP100.ijm

• Purpose: Aligns and quantifies GFP and GP100 positive cells in serial sections.
• Features: Image alignment, tissue/ROI selection, cell segmentation, colocalization.
• Output: Quantification tables, annotated images.

IF_LamininCSA_v2.ijm

• Purpose: Quantifies muscle fiber cross-sectional area and nuclei centralization.
• Features: Fiber/nuclei segmentation, manual editing, classification.
• Output: Results tables, fiber/nuclei masks, annotated images.

IF_MMP10.ijm

• Purpose: Quantifies green intensity in manually selected cells.
• Features: Manual cell selection, segmentation, intensity measurement.
• Output: QuantificationResults.xls, annotated images.

IF_MP14.ijm / IF_MP14_2ch.ijm

• Purpose: 3D segmentation and quantification of nuclear/cytoplasmic proteins.
• Features: Multi-channel segmentation, volume/intensity measurement, shape descriptors.
• Output: Total.xls, analyzed images.

IF_MacrophageClass.ijm

• Purpose: Automatic classification of macrophage phenotypes.
• Features: Macrophage/phenotype marker segmentation, phenotype quantification.
• Output: IF_quantification.xls.

IF_MANUALCardio_v2.ijm

• Purpose: Manual selection and measurement of cardiomyocytes.
• Features: Manual ROI, nuclei detection, area/diameter measurement.
• Output: [image].xls, analyzed images.

IF_NanoTrack.ijm

• Purpose: Nanoparticle tracking in time-lapse images.
• Features: Preprocessing, artifact removal, TrackMate integration.
• Output: Track overlays, CSV tracks.

IF_NETs.ijm

• Purpose: Quantifies NETs (neutrophil extracellular traps) in confocal images.
• Features: K-means clustering, nuclei/NETs segmentation, cell classification.
• Output: Quantification_IF_NETs.xls, overlays.

IF_Neutrophils_N2.ijm

• Purpose: Quantifies neutrophil populations and N2-type neutrophils.
• Features: Nuclei/cytoplasm segmentation, marker quantification, ratio calculation.
• Output: QuantificationResults.xls.

IF_Neuron.ijm

• Purpose: Quantifies rafe structures in brain IF images.
• Features: Manual ROI selection, intensity measurement in regions.
• Output: Results.xls.

IF_Nucleolos.ijm / IF_NucleolosI&S.ijm

• Purpose: Quantifies nucleoli intensity and shape in IF confocal images.
• Features: Channel selection, nuclei/nucleoli segmentation, shape descriptors.
• Output: Quantification_IntensityResults.xls, ShapeDescriptors.xls.

IF_NuclCyto.ijm / IF_nuclCyto_v3.ijm / IF_NuclCyto_Phenotype.ijm

• Purpose: Quantifies nuclear and cytoplasmic IF signal.
• Features: Nuclei/cell segmentation, marker quantification in compartments.
• Output: QuantificationResults_IF_NuclCyto.xls, QuantificationResutls_IF_NuclCyto_Phenotype.xls.

IF_PhenotypeQuant.ijm

• Purpose: Automatic classification of cell phenotypes (single channel).
• Features: Preprocessing, thresholding, area/intensity quantification.
• Output: Quantification_[phName].xls.

IF_Soma_and_axon_size_2D.ijm

• Purpose: Quantifies soma and axon size in 2D neuron images.
• Features: Manual ROI, soma annotation, prolongation area measurement.
• Output: Quantification_Somas_and_Prolongations.xls, analyzed images.

IF_TrombosM1M2.ijm

• Purpose: Quantifies nuclei and cells positive for green/red markers.
• Features: Nuclei/cell segmentation, marker quantification, density calculation.
• Output: QIF_results.xls, analyzed images.

IF_Vessels_Platelets.ijm

• Purpose: Quantifies vessels and platelets in IF images.
• Features: Vessel/platelet segmentation, area/count measurement.
• Output: Quantification tables, overlays.

IF3D_BiofilmQuantification.ijm

• Purpose: Volumetric analysis of 3D bacteria biofilm in confocal stacks.
• Features: Single file and batch mode; interactive thresholding; quantifies biofilm volume, density, surface area, roughness, and thickness; supports ROI management.
• Output: QuantificationResults_IF3D_BiofilmQuantification.xls, segmented images with overlays.

IF3D_BiofilmQuantification_Cocultivos.ijm

• Purpose: Volumetric analysis of 3D cocultured bacteria biofilms with two markers.
• Features: Single file and batch mode; interactive thresholding; quantifies total biofilm and individual marker volumes, ratios, surface area, roughness, and thickness.
• Output: Results_IF3D_BiofilmQuantification_Colultivos.xls, segmented images with overlays.

IF3D_BiofilmQuantification_LiveDead.ijm

• Purpose: Volumetric analysis of live and dead bacteria in 3D biofilms.
• Features: Single file and batch mode; interactive thresholding for live and dead channels; quantifies total, live, and dead biofilm volumes and ratios; slice-wise density analysis.
• Output: IF3D_BiofilmQuantification_LiveDead.xls, segmented images for live, dead, and total biofilm.

IF3D_CellCount.ijm

• Purpose: Counts nuclei/cells in 3D confocal stack images.
• Features: Single file and batch mode; channel selection; adjustable min/max particle size; 3D segmentation; volume and intensity statistics.
• Output: Quantification_IF3D_CellCount.xls, labeled images with overlays.

IF3D_CellNucleusAlignment.ijm

• Purpose: Quantifies 2D morphology and orientation of nuclei in 3D stacks.
• Features: Single file mode; user selects DAPI and fiber channels; reference fiber orientation; StarDist segmentation; computes area, aspect ratio, circularity, Feret diameter, and relative angle.
• Output: Excel file with morphology/orientation metrics, analyzed images with overlays.

IF3D_Centriolos.ijm

• Purpose: Quantifies centrioles and their integrated intensity in 3D IF stacks.
• Features: User selects ROI; processes red channel; computes average and max intensity per slice; detects foci using 3D Objects Counter.
• Output: Total.xls with number of foci and intensity metrics, 3D viewer visualization.

IF3D_Coloc2NuclearProts.ijm

• Purpose: 3D colocalization analysis of two nuclear proteins with speckle/foci structures.
• Features: Single file and batch mode; channel selection; automatic segmentation; computes number and volume of speckles, colocalization, and ratios per cell.
• Output: QuantificationResults_IF3D_Coloc2NuclearProts.xlsx, analyzed images, ROI sets.

IF3D_FibrinMesh3D.ijm

• Purpose: 3D segmentation and quantification of fibrin mesh in confocal images.
• Features: User selects number of Z-slices; background subtraction; steerable filtering; ridge detection; BoneJ analysis for volume, density, thickness, and fiber length.
• Output: Quantification_FibrinMesh.xls, segmentation images, overlays.

IF3D_ManguitoRotador.ijm

• Purpose: Quantifies nuclei and epigenetic marker signal in 3D stacks of rotator cuff tissue.
• Features: Batch mode; channel selection; projection and slice-wise segmentation; computes nuclear volume and average marker intensity.
• Output: Quantification_Global.xls, Quantification_IndividualCells.xls, analyzed images.

IF3D_MicrogliaMarkerColoc.ijm

• Purpose: 3D qualitative colocalization of microglia and protein markers in confocal stacks.
• Features: Single file and batch mode; channel selection; vessel exclusion; segmentation and quantification of marker volumes and colocalization ratio.
• Output: Colocalization_Results.xls, segmented images, overlays.

IF3D_NeuralFiberColoc.ijm

• Purpose: 3D colocalization analysis of neuron fibers and protein markers.
• Features: Single file and batch mode; channel selection; thresholding and size filtering; quantifies marker volumes and colocalized volume; saves ROI sets.
• Output: Colocalization_Results.xls, analyzed images, ROI sets.

IF3D_NeuralPhagocytosis.ijm

• Purpose: 3D colocalization quantification of neuron and phagocyte markers.
• Features: Single file and batch mode; channel selection; thresholding and size filtering; volumetric and surface colocalization analysis.
• Output: IF3D_NeuronPhagocytosis (Surface or Volumetric).xls, segmented images.

IF3D_NuclearDots.ijm

• Purpose: Automatic quantification of nuclear 3D structures (foci) per cell.
• Features: Single file and batch mode; channel selection; adjustable thresholds and size filters; nuclei and foci segmentation; per-cell foci counting.
• Output: QuantifiedRedFOCIs.xls, analyzed images.

IF3D_NuclCyto.ijm

• Purpose: Automatic segmentation and quantification of nuclei and cytoplasm in IF 3D images.
• Features: Single file and batch mode; adjustable thresholds; computes volumes and average intensities for nucleus, cytoplasm, and whole cell; percent saturated voxels.
• Output: QuantificationResutls_IF3D_NuclCyto.xls, analyzed images.

IF3D_WholeFieldIntensity.ijm

• Purpose: Quantifies protein intensity distribution in IF 3D stacks.
• Features: Single file and batch mode; channel selection; computes area, mean, std, min, max intensity; saves projections and segmented images.
• Output: ProteinQuantification.xlsx, segmented images.

IF4D_BiofilmQuantification.ijm

• Purpose: Quantification of live/dead bacteria in 4D (time-lapse) confocal images.
• Features: Single file and batch mode; interactive thresholding; supports time series and volumetric analysis; computes biofilm volume, density, thickness, and surface area over time.
• Output: QuantificationResults_IF3_BiofilmQuantification.xls, segmented images.

IF4D_ColocLiveInfection.ijm / IF4D_LiveInfection.ijm

• Purpose: Quantifies colocalization of lysosome and NP-GFP signals in 4D live infection confocal images.
• Features: Single file and batch mode; interactive thresholding; channel selection; computes total and colocalized volumes per frame; saves overlays.
• Output: InfectionResults.xls, segmented images with overlays.

PHC3D_OrganoidCount.ijm

• Purpose: Quantification of organoids in phase contrast 3D Z-stack images (Button Device).
• Features: Single file and batch mode; user-defined parameters (resolution, min/max organoid size, circularity filter); automated background removal and artifact exclusion; 3D segmentation; connected components labeling and 3D region analysis.
• Output: QuantifiedImages.xls (image label, number of organoids, average size, std size); labeled/segmented images in AnalyzedImages folder.

PHC_MonocyteCount.ijm

• Purpose: Quantification of monocytes in brightfield tissue images.
• Features: Automatic tissue detection and segmentation; manual ROI editing (add/remove tissue areas); automatic monocyte detection via maxima finding; manual correction of detected monocytes.
• Output: Quantification_Monocytes.xls (image label, number of monocytes, tissue area); analyzed images with overlays.

PHC_ClustersArea.ijm

• Purpose: Quantification of clustered cell areas in phase contrast microscopy images.
• Features: Single file and batch mode; user-defined parameters (resolution, min cluster size, cluster splitting tolerance); gradient-based segmentation; morphological filtering; background subtraction; watershed for cluster splitting.
• Output: Results_PHC_ClusterArea.xls (image label, number of clusters, cluster IDs, area); segmented/annotated images in AnalyzedImages.

WSI_TRAP.ijm

• Purpose: Quantification of positive area in TRAP-stained WSI images.
• Features: Manual ROI selection; color thresholding for positive area; area measurement and ratio calculation.
• Output: Total.xls (label, total area, positive area, % ratio); analyzed images with overlays.

WSI_TranswellCells.ijm

• Purpose: Quantification of high-intensity stained cells in transwell brightfield images.
• Features: Single file and batch mode; user-defined thresholds for tissue and cell segmentation, min cell size; automatic tissue and cell detection.
• Output: QuantificationResults_WSI_TransWellCells.xls (label, tissue area, cell area, ratio); analyzed images with overlays.

WSI_Steatosis.ijm

• Purpose: Quantification of steatosis (fat deposits) in WSI images.
• Features: Single file and batch mode; user-defined thresholds for tissue and fat, min/max fat size, texture/circularity filters; texture-based separation of fat/glucogen.
• Output: QuantificationResults_Steatosis.xls (label, tissue area, steatosis area, ratio); segmented/annotated images.

WSI_SiriusRedPerivascular.ijm

• Purpose: Quantification of Sirius Red IHC in WSI images (perivascular analysis).
• Features: Single file mode; interactive thresholding; background correction (global/tissue); color deconvolution for Sirius Red; manual ROI editing; batch mode (commented).
• Output: SiriusRed_QuantificationResults.xls (label, tissue area, positive area, ratio); analyzed images, ROI sets.

WSI_SiriusRedCardio.ijm

• Purpose: Quantification of Sirius Red IHC in cardiac WSI images.
• Features: Single file mode; interactive thresholding; manual ROI editing; color deconvolution for Sirius Red.
• Output: QuantificationResults_SiriusRedCardio.xls (label, tissue area, positive area, ratio); analyzed images, ROI sets.

WSI_SiriusRed.ijm

• Purpose: Quantification of Sirius Red IHC in WSI images.
• Features: Single file and batch mode; interactive or automatic workflow; color deconvolution; background compensation; manual ROI editing.
• Output: QuantificationResults_SiriusRed.xls (label, tissue area, positive area, ratio); analyzed images, ROI sets.

WSI_SiriusRed_Manual.ijm

• Purpose: Manual quantification of Sirius Red IHC in WSI images.
• Features: Single file and batch mode; interactive thresholding; manual ROI editing; color deconvolution.
• Output: QuantificationResults_SiriusRed.xls (label, tissue area, positive area, ratio); analyzed images, ROI sets.

WSI_PAS_Transwell.ijm

• Purpose: Quantification of PAS-stained cells in transwell images.
• Features: Single file and batch mode; user-defined threshold for PAS-positive segmentation, min cell size; automatic parameter optimization (optional); manual ROI editing.
• Output: QuantificationResults_WSI_PAS_Transwell.xls (label, tissue/PAS thresholds, tissue area, PAS area, ratio); analyzed images.

WSI_NecrosisHE.ijm

• Purpose: Quantification of necrosis in H&E-stained WSI images.
• Features: User-defined thresholds for tissue, haematoxylin, eosin; manual ROI editing (add/remove tissue/necrosis); color deconvolution for H&E.
• Output: NecrosisQuantification.xls (label, tissue area, necrosis area, ratio); analyzed images with overlays.

WSI_MMPsafranin.ijm

• Purpose: Quantification of red safranin-stained area in WSI images.
• Features: Manual ROI selection; HSB color thresholding for safranin; area measurement and ratio calculation.
• Output: Total.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_LymphTcount.ijm

• Purpose: Quantification of CD3+ T lymphocytes in HE+CD3 WSI images.
• Features: Automatic tissue/nuclei segmentation; color deconvolution for brown marker; user-defined thresholds and size filters; batch and single file mode.
• Output: Total.xls (label, number of cells, number of lymphT cells, tissue area); analyzed images with overlays.

WSI_LungTissueCD3.ijm

• Purpose: Quantification of CD3+ cells in lung WSI images.
• Features: Automatic tissue/nuclei segmentation; color deconvolution for brown marker; user-defined thresholds, min % CD3+ for positive cells; batch and single file mode.
• Output: Quantification_CD3count.xls (label, number of total cells, number of CD3+ cells, tissue area); analyzed images with overlays.

WSI_LungNodulesHE.ijm

• Purpose: Quantification of lung nodules in H&E-stained WSI images.
• Features: Automatic tissue/nodule segmentation; manual ROI editing (delete tissue/nodules); color deconvolution for H&E.
• Output: Total.xls (label, tissue area, nodule area, ratio); analyzed images with overlays.

WSI_LeucoCount.ijm

• Purpose: Quantification of CD45+ leucocytes in HE+CD45 WSI images.
• Features: Automatic tissue/nuclei segmentation; color deconvolution for brown marker; user-defined thresholds and size filters; batch and single file mode.
• Output: Total.xls (label, number of cells, number of leucocytes, tissue area); analyzed images with overlays.

WSI_EosinIntensity.ijm

• Purpose: Quantification of eosin intensity in selected regions of WSI images.
• Features: Manual ROI selection (multiple regions); automatic tissue segmentation; color deconvolution for eosin.
• Output: EosinQuantification.xls (label, measured tissue area, eosin intensity); analyzed images with overlays.

WSI_Desmin.ijm

• Purpose: Quantification of brown desmin-stained area, removing nonspecific vessel staining.
• Features: Automatic tissue/vessel detection; color deconvolution for brown marker; user-defined vessel exclusion size; batch and single file mode.
• Output: ResultsCuantificacionDesmina.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_DABintensity_ROIs.ijm

• Purpose: Quantification of DAB intensity in manually drawn ROIs.
• Features: Manual ROI selection (multiple areas); background subtraction; color deconvolution for DAB; area and intensity measurement.
• Output: Results.xls (label, area of analysis, DAB+ area, DAB+ avg intensity); analyzed images with overlays.

WSI_CD3count.ijm

• Purpose: Quantification of CD3+ cells in WSI images.
• Features: Automatic tissue/nuclei segmentation; color deconvolution for brown marker; user-defined thresholds, min % CD3+ for positive cells; batch and single file mode.
• Output: Quantification_CD3count.xls (label, number of total cells, number of CD3+ cells, tissue area); analyzed images with overlays.

WSI_CapillaryDAB.ijm

• Purpose: Quantification of DAB-stained capillaries in WSI images.
• Features: Manual ROI selection for analysis area; automatic tissue/capillary detection; color deconvolution for DAB; manual editing of detected capillaries.
• Output: Quantification_capillaries_area.xls (label, analyzed tissue area, capillary area, ratio, number of capillaries); Quantification_capillaries.xls (label, analyzed tissue area, number of capillaries); analyzed images with overlays.

WSI_BrownDetectionArea.ijm

• Purpose: Quantification of brown-stained area in WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds; batch and single file mode.
• Output: Total.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Spleen.ijm

• Purpose: Quantification of brown-stained area in spleen WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: Resutls_WSI_BrownDetectionArea_Spleen.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Muscle.ijm

• Purpose: Quantification of brown-stained area in heart/muscle WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds; batch and single file mode.
• Output: Results_WSI_BrownDetectionArea_Muscle.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Lung.ijm

• Purpose: Quantification of brown-stained area in lung WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: Results_WSI_BrownDetectionArea_Lung.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Liver.ijm

• Purpose: Quantification of brown-stained area in liver WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds, min vessel size; batch and single file mode.
• Output: Results_WSI_BrownDetectionArea_Liver.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Heart.ijm

• Purpose: Quantification of brown-stained area in heart WSI images.
• Features: Automatic tissue and brown marker segmentation; tubeness filtering for tissue; user-defined thresholds; batch and single file mode.
• Output: Results_WSI_BrownDetectionArea_Heart.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Brain.ijm

• Purpose: Quantification of brown-stained area in brain WSI images.
• Features: Automatic tissue and brown marker segmentation; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: Results_WSI_BrownDetectionArea_Brain.xls (label, tissue area, stained area, ratio); analyzed images with overlays.

WSI_BrownDetectionArea_Brain_NHI_SMI38.ijm

• Purpose: Quantification of brown-stained area in brain WSI images (NHI/SMI38).
• Features: Automatic tissue and brown marker segmentation; background correction; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: WSI_BrownArea_Brain_Results.xls (label, tissue area, stained area, ratio, background/HE/marker stats); analyzed images with overlays.

WSI_BrownDetectionArea_Brain_MBP.ijm

• Purpose: Quantification of brown-stained area in brain WSI images (MBP).
• Features: Automatic tissue and brown marker segmentation; background correction; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: WSI_BrownArea_Brain_Results.xls (label, tissue area, stained area, ratio, background/HE/marker stats); analyzed images with overlays.

WSI_BrownDetectionArea_Brain_IBA1.ijm

• Purpose: Quantification of brown-stained area in brain WSI images (IBA1).
• Features: Automatic tissue and brown marker segmentation; background correction; user-defined thresholds, min stained particle size; batch and single file mode.
• Output: WSI_BrownArea_Brain_Results.xls (label, tissue area, stained area, ratio, background/HE/marker stats); analyzed images with overlays.

WF_ClusterArea.ijm

• Purpose: Quantification of clustered cell areas in widefield phase contrast microscopy images.
• Features: Single file and batch mode; user-defined parameters (resolution, min cluster size); segmentation and area measurement.
• Output: Results_PHC_ClusterArea.xls (image label, number of clusters, cluster IDs, area); segmented/annotated images.