Functionality#

ClearMap 2.0 is designed for advanced and fast image processing of large (Terabyte) 3D data sets [Kirst2020] obtained from tissue clearing. It is a complete redesign of ClearMap 1.0 [Renier2016].

The modular source management allows a unified handling of various data sources (image files, binary files, memory maps, shared memory arrays, numpy arrays, GPU arrays, graph formats) and fast parallel 3D image and graph processing as well as interactive visualization of large 3d images.

In the following we provide a list of the ClearMap 2.0 functionality. ClearMap 2.0 is open source software and available for download under ChristophKirst/ClearMap2.

Modules#

The ClearMap code is structured into these main modules:

  • Alignment for stitching, resampling, and registration of images onto references

  • Analysis for measurements and statistical analysis of the data

  • ImageProcessing for correcting and quantifying image data

  • IO for reading and writing data

  • ParallelProcessing for organizing parallel processing of the data

  • Visualization for visualizing the data and results.

Alignment#

The ClearMap.Alignment module includes methods for

Analysis#

The Analysis module provides methods for the quantification and analysis of the data, including:

  • graph and network analysis for very large graphs

    enabling:
    • graph preprocessing and cleanup

    • graph branch reduction

    • graph annotation

    • graphs embedded 3d space

    • 3d graphs with 3d edge geometry

    • graph vertex and edge label morphological operations

    • sub-graph extraction

    • network analysis

  • measurement routines to quantify the results

    enabling:
    • measurements of expression levels

    • measurements of geometric shapes and radii

    • maxima detection

    • voxelization

  • statistics routines to analyse the results

ImageProcessing#

The ImageProcessing module is the core of ClearMap and provides methods to process 3d images of TB size, including:

IO#

The IO module provides methods to hold information about data and read and write data files efficiently as sources and sinks, that can be sliced and turned into virtual sources useful for parallel processing and memory mapping:

Supported source types include:

Format

Module

Description

tif

TIF

tif images and stacks

raw / mhd

MHD

raw image files with optional mhd header file

nrrd

NRRD

nearly raw raster data files

csv

CSV

text files as comma separated values

npy

NPY

numpy binary file

gt

GT

graph tool file

file list

FileList

folder, file list or file expression for a list source files

MMP

memory mapped file

SMA

shared memory array

The IO module also provides a workspace that handles the organization of files in CellMap and TubeMap projects:

ParallelProcessing#

The ParallelProcessing module provides methods for distributed processing.

Visualization#

ClearMap’sClearMap.Visualization module comes with a larger set of interactive visualization functions using various backends:

  • Qt

    providing:

    • fast interactive 2d slice plotting of 3d TB data sets

    • overlays and/or synchronized window display of multiple data sets.

  • Vispy

    providing:

    • 3d volume rendering, lists and line plots

    • 3d plots 3d graphs as lines or mesh plots with edge geometries, etc