ClearMap is a toolbox for the analysis and registration of volumetric data from cleared tissues.

ClearMap has been designed to analyze large 3D image stack datasets obtained with Light Sheet Microscopy of iDISCO+ cleared mouse brains samples immunolabeled for nuclear proteins. ClearMap can perform image registration to a 3D annotated reference (such as the Allen Institute Brain Atlases), volumetric image processing, object detection and statistical analysis. The tools in ClearMap have been written with the mapping of Immediate Early Genes in the brain as the primary application.

However, these tools should also be more broadly useful for data obtained with other types of microscopes, other types of markers, and other clearing techniques. Moreover, the registration and region segmentation capabilities of ClearMap are not depending on the Atlases and annotations we used in our study. Users are free to import their own reference files and annotation files, so the use of ClearMap can be expanded to other species, and other organs or samples.

ClearMap is written in Python 2.7, and is designed to take advantage of parallel processing capabilities of modern workstations. We hope the open structure of the code will enable in the future many new modules to be added to ClearMap to broaden the range of applications to different types of biological objects or structures.

Author and License


ClearMap lead programming and design:

Christoph Kirst, The Rockefeller University

Scripts and specific applications:

Nicolas Renier and Christoph Kirst The Rockefeller University


Christoph Kirst and Nicolas Renier The Rockefeller University

Additional programming and consulting:

Kannan Umadevi Venkataraju Cold Spring Harbor Laboratories



See LICENSE or for details.

ClearMap functions