About

About CytoCV

CytoCV is a browser-based research workflow for DeltaVision microscopy of yeast cells, with connected image inputs, software-generated measurements, results, and links to deeper technical and biological context.

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Table of contents

  1. Overview
  2. Research Need
  3. Workflow
  4. Measurements
  5. Image Inputs
  6. Results
  7. Biological Value

Overview

What CytoCV Is

CytoCV is a browser-based analysis workflow for supported .dv, .tif, and .tiff microscopy files of yeast cells. It is designed to keep image files, channel interpretation, scale context, and measurement outputs connected in one place.

The platform supports a modern default analysis path centered on structural segmentation plus red and green fluorescence measurements, while keeping older Blue-based workflows available as legacy paths when they are needed.

  • DIC Structure
    Structural channel used for segmentation.
  • Red Red
    Red-signal contour and distance context.
  • Green Green
    Green intensity and dot measurements.
  • Blue Legacy
    Legacy nucleus-related analysis path.

Research Need

Why Researchers Need It

Microscopy studies often require researchers to inspect many cells, compare fluorescent signals, and record measurements across repeated experiments. Doing that cell by cell by eye can take time, scale poorly as datasets grow, and introduce inconsistency in how cells are selected or measured.

CytoCV is designed to reduce manual analysis time and help researchers quantify more cells more consistently, while preserving the connection between the original image stack and the numbers produced from it.

Workflow

How the Workflow Works

Researchers upload supported .dv, .tif, or .tiff files, and CytoCV validates the channels required for the selected workflow before analysis proceeds. Preview images are generated first so the file can be checked before segmentation and measurement begin.

The platform then uses DIC, a structural brightfield-like channel, as the input for Mask R-CNN-based cell segmentation. After cells are segmented, CytoCV computes software-generated per-cell measurements and presents the results for review and export.

Workflow outputs

  • Validation
  • Preview images
  • Segmentation
  • Per-cell measurements
  • Review
  • Export

Measurements

What CytoCV Measures

  • Puncta Distance Reports the distance between paired puncta and the opposite-channel integrated intensity sampled along the line between them.
  • Cen Dot Location and Biorientation Reports CEN dot location relative to segmented cell geometry and separately counts Green dots as colinear or off-axis relative to the Red puncta axis.
  • Green and Red contour intensity summaries Calculates contour-based integrated intensity summaries across the Red and Green channels.
  • Nuclear and Cell-Pair Intensity Uses either a Red-defined or Green-defined contour as the active nucleus source and measures the opposite channel inside the nucleus and across the cell pair.
Blue-based measurements remain available as legacy workflows, not the main default path.

Image Inputs

What Image Inputs CytoCV Expects

CytoCV is built for single-file microscopy stacks from yeast imaging workflows. The platform accepts .dv, .tif, and .tiff files that preserve the channel context needed for validation, preview generation, segmentation, and downstream measurement.

In the current workflow, DIC provides the structural input for cell segmentation, while Red and Green channels provide the main fluorescence measurements. Blue remains relevant for older legacy analyses when that path is needed. Biology-facing names such as DAPI, mCherry, and GFP commonly map to the logical Blue, Red, and Green roles.

  • File format .dv, .tif, and .tiff image stacks are supported upload formats.
  • Structural input DIC is used as the segmentation input that defines cell boundaries and mother/daughter geometry.
  • Fluorescence channels Red and Green support the main current measurement workflows, while Blue remains available for legacy analysis paths.
  • Validation context Uploads are checked before processing so the workflow can confirm the expected input structure and generate preview imagery for review.

Results

Review and Export Results

CytoCV keeps segmented-cell review tied to the image context so users can inspect overlays, compare calculated per-cell outputs, and confirm that detected cells match the structures they expect before finalizing a run.

After review, the platform supports exportable result tables for downstream analysis, comparison across experiments, and figure-building workflows outside the browser.

Biological Value

Why This Matters Biologically

These measurements can help researchers compare signal localization between the nucleus and the cell pair, examine how paired Red and Green signals relate to one another, and evaluate patterns across yeast populations or mutant conditions.

CytoCV supports that interpretation by producing repeatable software measurements tied to the source images, but the results should still be reviewed alongside image context, experimental controls, and biological judgment.

Read the deeper biological context for assay-level explanations of chromosome segregation, puncta distance, CEN dot location, biorientation, and nuclear intensity workflows.