dropdrop 1.1.0

Python pipeline script for detecting droplets with beads and other inclusions via cellpose

DropDrop

Automated Python pipeline for detecting droplets and inclusions (beads) in microscopy z-stacks using Cellpose segmentation and morphological analysis.

Tailored for the EVOS M5000 Imaging System.

Installation

# Using uv (recommended)
uv pip install dropdrop

# Using pip
pip install dropdrop

From source

git clone https://github.com/yourusername/dropdrop.git
cd dropdrop
uv pip install -e .

Quick Start

# Run with interactive prompts
dropdrop ./images

# Run with settings
dropdrop ./images --settings "d=1000,p=on,l=experiment1"

# Process only first 5 frames (for testing)
dropdrop ./images -n 5

Usage

Basic Commands

# Run pipeline with compact settings
dropdrop ./images --settings "d=1000,p=on,c=6.5e5,l=experiment1"

# Custom output directory
dropdrop ./images ./results/my_project --settings "d=500"

# Interactive viewer (view results after processing)
dropdrop ./images --view

# Interactive editor (manually correct inclusions)
dropdrop ./images --interactive

# Archive output as tar.gz
dropdrop ./images -z

Settings Format

Compact settings string: d=dilution,p=poisson,c=count,l=label

Key	Full name	Description	Default
d	dilution	Dilution factor	500
p	poisson	Enable Poisson analysis (on/off)	on
c	count	Stock bead count per uL	6.5e5
l	label	Project label for output naming	None

Cache Control

dropdrop ./images --no-cache       # Disable caching
dropdrop ./images --clear-cache    # Clear cache before run

Interactive Editor

The editor allows manual correction of detected inclusions:

Key	Action
Left-click	Add inclusion
Right-click (hold)	Remove inclusions
s	Toggle droplet selection (hover over droplet)
u	Undo last action
c	Clear all inclusions in frame
d	Toggle droplet visibility
Arrow keys / Space	Navigate frames
q / Esc	Exit

Disabled droplets (gray with X) are excluded from the final results.

Output Structure

results/<YYYYMMDD>_<label>/
  data.csv                  # Raw detection data
  summary.txt               # Settings and statistics
  size_distribution.png     # Droplet diameter histogram
  poisson_comparison.png    # Bead distribution vs theoretical

data.csv columns

Column	Description
frame	Frame index
droplet_id	Droplet ID within frame
center_x, center_y	Droplet center coordinates (px)
diameter_px, diameter_um	Droplet diameter
area_px, area_um2	Droplet area
inclusions	Number of inclusions detected

Configuration

Create config.json in your working directory to customize detection parameters:

{
  "cellpose_flow_threshold": 0.4,
  "cellpose_cellprob_threshold": 0.0,
  "erosion_pixels": 5,
  "kernel_size": 7,
  "tophat_threshold": 30,
  "min_inclusion_area": 7,
  "max_inclusion_area": 50,
  "edge_buffer": 5,
  "min_droplet_diameter": 80,
  "max_droplet_diameter": 200,
  "px_to_um": 1.14,
  "cache": {
    "enabled": true,
    "max_frames": 100
  }
}

Parameters

Parameter	Description
cellpose_flow_threshold	Cellpose flow threshold for segmentation
cellpose_cellprob_threshold	Cellpose cell probability threshold
erosion_pixels	Pixels to erode droplet mask before inclusion detection
kernel_size	Morphological kernel size for black-hat transform
tophat_threshold	Threshold for inclusion detection
min/max_inclusion_area	Inclusion size constraints (px)
edge_buffer	Buffer from image edge to ignore inclusions
min/max_droplet_diameter	Droplet size constraints (px)
px_to_um	Pixel to micrometer conversion factor

Requirements

• Python 3.12+
• CUDA-capable GPU (recommended for Cellpose)

License

MIT