LBM-Suite2p-Python Documentation

LBM-Suite2p-Python Documentation#

A volumetric 2-photon calcium imaging processing pipeline for Light Beads Microscopy (LBM) datasets.

What is LBM-Suite2p-Python?#

This package processes multi-plane calcium imaging data through a three-step workflow:

  1. Convert raw TIFF files to binary format for Suite2p

  2. Process each z-plane independently (registration → segmentation → extraction)

  3. Aggregate planar results into volumetric outputs with visualization

Key capabilities:

  • Planar-by-planar Suite2p processing optimized for volumetric datasets

  • Automatic detection and merging of ScanImage multi-ROI acquisitions

  • Robust binary validation to avoid redundant reprocessing

  • Post-processing filters for cell quality (area, eccentricity, event exceptionality)

  • ΔF/F calculation with multiple baseline methods

  • Volumetric statistics and Rastermap clustering

Quick Navigation#

See the user guide for complete examples and parameter tuning.

Quick Example#

import mbo_utilities as mbo
import lbm_suite2p_python as lsp

data_dir = r"path/to/data"
files = mbo.get_files(data_dir, "tiff", max_depth=3)

# Extract metadata and create ops
metadata = mbo.get_metadata(files[0])

# Process entire volume
output_ops = lsp.run_volume(
    input_files=files,
    save_path=save_dir,
    ops=ops
)

Helpful Suite2p Resources#

Topic

Resource

Description

Parameters

Suite2p Settings

Complete parameter reference

Detection

ROI Detection

Video overview of detection algorithm

Registration

Issue #921

Troubleshooting registration artifacts

Critical Parameters

Issue #129

Discussion of tau, threshold_scaling, diameter

ROI Overlap

Issue #851

Understanding max_overlap and allow_overlap

Fluorescence Signals

Issue #627

Explanation of F and Fneu outputs

Citation#

If you use this pipeline, please cite Suite2p and LBM:

Acknowledgements#

This pipeline is built on excellent open-source tools:

  • Suite2p - Registration and segmentation (Pachitariu, Stringer, et al.)

  • Cellpose - Anatomical segmentation (Stringer, Pachitariu, et al.)

  • Rastermap - Activity clustering (Stringer, Pachitariu)

  • scanreader - ScanImage metadata parsing (atlab)

  • Suite3D - Volumetric processing inspiration (Haydaroglu)

Special thanks to the Miller Brain Observatory team and all contributors for testing and feedback.

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