microProfiler 0.8.1

Microscopy image preprocessing, segmentation, and profiling pipeline

microProfiler

Microscopy image preprocessing, segmentation, and profiling pipeline for multi-well plate data.

Converts vendor-specific formats (Operetta, MICA) into a unified file structure, then provides a chainable preprocessing pipeline followed by cell segmentation and feature profiling.

Requirements

• Python: >= 3.10
• Conda (recommended) or pip

Quick setup (conda)

# Create environment from curated file
conda env create -f micro.yml
conda activate micro

# Install microProfiler
pip install -e .

# With dev extras (testing, linting)
pip install -e ".[dev]"

Manual setup (conda or pip)

# Conda
conda create -n micro python=3.10
conda activate micro
conda install -c conda-forge cellpose numpy pandas scipy scikit-image tifffile torch jax pyyaml tqdm
pip install natsort "pydantic>=2.0"
pip install -e .

Pipeline Order

Input → Convert[+resize] → Resize → BaSiC → Z-projection → Tile → Segment → Profile

1. Convert — Vendor-format files → {well}_f{field}_z{z}_t{t}_ch{ch}.tiff (optional resize during write)
2. Resize (optional) — Standalone resize step (after conversion if --resize is set)
3. BaSiC correction (optional) — Flatfield/darkfield shading correction
4. Z-projection (optional) — Max/mean/min projection of Z-stacks
5. Tile splitting (optional) — Non-overlapping tiles
6. Segmentation (optional) — Cellpose-SAM object detection
7. Profiling — Image-level and object-level feature extraction

Quick Start (CLI)

# Run full pipeline using a YAML config file
microprofiler run /path/to/Measurement\ 1 --config examples/pipeline_config.yml

# Run full pipeline on Operetta format (with resize during conversion and standalone resize)
microprofiler run /path/to/Measurement\ 1 --format operetta \
  --convert-resize 0.5 \
  --resize 0.5 \
  --basic fit-transform \
  --z-projection max \
  --tile 540 540 \
  --segment cell --segment-channels ch1 \
  --profile-image \
  --profile-object cell

# Run full pipeline on MICA format
microprofiler run /path/to/Sequence\ 002 --format mica \
  --segment cell --segment-channels ch1 \
  --profile-object cell --db results.db

# Convert only (with optional resize and custom output name)
microprofiler convert /path/to/Measurement\ 1 --format operetta --convert-resize 0.5 --output-name myoutput

# Opt out of in-place processing; opt into vendor file deletion
microprofiler run /path/to/Measurement\ 1 \
  --no-basic-inplace --no-zproject-inplace --no-tile-inplace \
  --delete-original

A complete YAML config with all parameters is at examples/pipeline_config.yml.

Library Usage

from pathlib import Path
from microProfiler.preprocessing.converter import convert_measurement
from microProfiler.preprocessing.z_projection import z_project_dataset
from microProfiler.preprocessing.tile_splitter import tile_dataset
from microProfiler.segmentation.cellpose import segment_dataset
from microProfiler.profiling.image_profiler import profile_images
from microProfiler.profiling.object_profiler import profile_objects

root = Path(r"/path/to/Measurement 1")

# 1. Convert to unified naming (optional resize during conversion)
#    Returns an ImageDataset pointing to "image/" by default
ds = convert_measurement(root, vendor_format="operetta", resize_factor=0.5)

# 3. Preprocess (in-place by default — files updated in the images/ directory)
ds = z_project_dataset(ds, root_dir=root, method="max")
ds = tile_dataset(ds, root_dir=root, tile_w=540, tile_h=540)

# 4. Segment
ds = segment_dataset(ds, object_name="cell", chan1=["ch1"])

# 5. Profile
profile_images(ds, db_path=root / "results.db", table_name="image")
profile_objects(ds, mask_name="cell",
                intensity_channels=["ch1", "ch2"],
                db_path=root / "results.db")

Supported Vendor Formats

Format	Input Pattern	Output Pattern
Operetta	r{row}c{col}f{field}p{stack}-ch{channel}sk{timepoint}fk1fl1.tiff	{well}_f{field}_z{stack}_t{timepoint}_ch{channel}.tiff
MICA	{row}/{col}/Pos{field}.tif	{well}_f{field}_z1_t1_ch{channel}.tiff

Key Modules

Module	Purpose
io.dataset	Metadata management, filename parsing, image loading
io.database	Thread-safe SQLite (WAL mode, per-thread connections)
io.loaders	Unified TIFF I/O + lazy intensity normalization
preprocessing.converter	Vendor → unified naming
preprocessing.z_projection	Z-stack projection
preprocessing.tile_splitter	Image tiling
preprocessing.basic_correction	BaSiC shading correction wrapper
segmentation.cellpose	Cellpose-SAM segmentation
profiling.image_profiler	Whole-image features
profiling.object_profiler	Per-object features (shape, intensity, texture)
profiling.extras	Radial distribution, granularity, GLCM, correlation

API Documentation

See docs/api.md for the full API reference including class and function signatures, parameters, and return types.

Acknowledgements

microProfiler uses the following open-source libraries:

• BaSiCPy — Flatfield and darkfield shading correction.
  https://github.com/peng-lab/BaSiCPy
• Cellpose — Generalist deep learning model for segmentation.
  https://github.com/MouseLand/cellpose

Citation

If you use microProfiler in your research, please cite the repository:

@software{microProfiler,
  author = {Hao He},
  title = {microProfiler: Microscopy Image Preprocessing, Segmentation, and Profiling Pipeline},
  year = {2026},
  url = {https://github.com/soulong/microProfiler}
}

License

MIT