CLARITY whole-brain segmentation

There are multiple segmentation functions for different data (stains/channels):

• virus

• cFos

• sparse

• nuclear

The segmentation workflow relies on an output from the registration workflow, but the segmentation wrapper function can be run without running the registration workflow.

This workflow performs the following tasks:

1. Segments neurons in cleared mouse brain of sparse or nuclear stains in 3D

2. Voxelizes segmentation results into density maps with Allen Atlas resolution

3. Computes features of segmented image and summarizes them per label

It executes:

seg/miracl_seg_clarity_neurons_wrapper.sh
seg/miracl_seg_voxelize_parallel.py
seg/miracl_seg_feat_extract.py

Main outputs

File	Description
segmentation/seg.{tif,mhd} or seg_nuclear.{tif,mhd}	Segmentation image with all labels (cells)
segmentation/seg_bin.{tif,mhd} or seg_bin_nuclear.{tif,mhd}	Binarized segmentation image
voxelized_seg.{tif,nii}	Segmentation results voxelized to ARA resolution
voxelized_seg_bin.{tif,nii}	Binarized version
clarity_segmentation_features_ara_labels.csv	Segmentation features summarized per ARA labels

Hint

Results can be opened in Fiji for visualization

GUI

Select from the main GUI menu (invoked from the cli: $ miraclGUI) or run:

$ miracl flow seg

The following window will appear:

Click on Select registered labels (..clar_vox.tif) in the reg_final dir to choose the registered labels annotation_hemi_{side}_**um_clar_vox.tif to summarize segmentation features where:

• {side} -> combined or split

• ** is the resolution -> 10, 25 or 50

The following window will appear:

Next, click on select input tiff dir to select folder with Thy1-YFP or other channel:

Lastly set the segmentation parameters:

Parameter	Description	Default
seg type	Channel type: virus cFos sparse (like Thy1 YFP) nuclear (like PI)	virus
channel prefix	Channel prefix and number if multiple channels. Example: Filter0001	None
labels voxel size	Registered labels voxel size in um: 10 25 50	10

Click Enter and Run to start the segmentation process.

Command-line

Usage:

$ miracl flow seg -f [ Tiff_folder ]

Example:

$ miracl flow seg -f my_tifs -t nuclear -s "-p C001" -e "-l reg_final/annotation_hemi_combined_25um_clar_vox.tif"

Arguments:

arguments (required):

  f. Input Clarity tif folder/dir [folder name without spaces]
  t. Channel type: sparse (like Thy1 YFP) or nuclear (like PI)

optional arguments (don't forget the quotes):

  Segmentation (invoked by -s " "):
    p. Channel prefix & number if multiple channels (like Filter0001)

  Feature extraction (invoked by -e " "):
    l. Allen labels (registered to clarity) used to summarize features
       reg_final/annotation_hemi_{hemi}_{vox}um_clar_vox.tif