MERFISH CCF mapped coordinates

Registration to the Allen CCFv3 was performed at 10 micron in-plane resolution. For each section, an anatomical label reference was created using class/subclass level cell type assignments which are then used to match to corresponding CCF parcellations. The midline was manually determined for each section to rotate the section upright and center in in the middle (section coordinates). This set of rectified images were stacked in sequential order to create an inital configuration for registration.

The ANTS registration framework was use to establish a 2.5D deformable spatial mapping between the MERFISH data and CCF coordinates via three major steps: (1) A 3D global affine (12 dof) mapping was performed to align the CCF into the MERFISH space (resampled CCF); (2) 2D affine (6 dof) registration of each MERFISH section to match the target resampled CCF section and finally, (3) a 2D multi-scale, symmetric diffeomorphic registration of each section (reconstructed coordinates) to finer level match to the target CCF section. Global and section-wise mappings from each of these registration steps were preserved and concatenated (with appropriate inversions) to allow point-to-point mapping between the original MERFISH coordinate space and the CCF space. See Yao et al for further details.

The purpose of this notebook is to provide an overview of how the mapped coordinate information is represented through example use cases.

You need to be connected to the internet to run this notebook and have run through the getting started notebook.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import SimpleITK as sitk
from pathlib import Path

from abc_atlas_access.abc_atlas_cache.abc_project_cache import AbcProjectCache

We will interact with the data using the AbcProjectCache. This cache object tracks which data has been downloaded and serves the path to the requsted data on disk. For metadata, the cache can also directly serve a up a Pandas Dataframe. See the getting_started notebook for more details on using the cache including installing it if it has not already been.

Change the download_base variable to where you have downloaded the data in your system.

download_base = Path('../../data/abc_atlas')
abc_cache = AbcProjectCache.from_cache_dir(download_base)

abc_cache.current_manifest

'releases/20241115/manifest.json'

Read in section, reconstructed and CCF coordinates for all cells

Read in the expanded cell metadata table we created in part 1 of the MERFISH tutorial. To keep track of the three type of coordinates we will rename the section coordinates (x,y,z) in the dataframe to (x_section, y_section, z_section)

cell = abc_cache.get_metadata_dataframe(directory='MERFISH-C57BL6J-638850', file_name='cell_metadata_with_cluster_annotation')
cell.rename(columns={'x': 'x_section',
                     'y': 'y_section',
                     'z': 'z_section'},
            inplace=True)
cell.set_index('cell_label', inplace=True)
cell.head(5)

	brain_section_label	cluster_alias	average_correlation_score	feature_matrix_label	donor_label	donor_genotype	donor_sex	x_section	y_section	z_section	neurotransmitter	class	subclass	supertype	cluster	neurotransmitter_color	class_color	subclass_color	supertype_color	cluster_color
cell_label
1019171907102340387-1	C57BL6J-638850.37	1408	0.596276	C57BL6J-638850	C57BL6J-638850	wt/wt	M	7.226245	4.148963	6.6	NaN	04 DG-IMN Glut	038 DG-PIR Ex IMN	0141 DG-PIR Ex IMN_2	0515 DG-PIR Ex IMN_2	#666666	#16f2f2	#3D53CC	#CC7A3D	#73FFBF
1104095349101460194-1	C57BL6J-638850.26	4218	0.641180	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.064889	7.309543	4.2	Glut	23 P Glut	235 PG-TRN-LRN Fat2 Glut	0953 PG-TRN-LRN Fat2 Glut_1	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63
1017092617101450577	C57BL6J-638850.25	4218	0.763531	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.792921	8.189973	4.0	Glut	23 P Glut	235 PG-TRN-LRN Fat2 Glut	0953 PG-TRN-LRN Fat2 Glut_1	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63
1018093344101130233	C57BL6J-638850.13	4218	0.558073	C57BL6J-638850	C57BL6J-638850	wt/wt	M	3.195950	5.868655	2.4	Glut	23 P Glut	235 PG-TRN-LRN Fat2 Glut	0953 PG-TRN-LRN Fat2 Glut_1	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63
1019171912201610094	C57BL6J-638850.27	4218	0.591009	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.635732	7.995842	4.4	Glut	23 P Glut	235 PG-TRN-LRN Fat2 Glut	0953 PG-TRN-LRN Fat2 Glut_1	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63

Next we read in the reconstructed coordinates and mapped parcellation from the metadata directory and join it to the cell dataframe

reconstructed_coords = abc_cache.get_metadata_dataframe(
    directory='MERFISH-C57BL6J-638850-CCF',
    file_name='reconstructed_coordinates',
    dtype={"cell_label": str}
)
reconstructed_coords.rename(columns={'x': 'x_reconstructed',
                                     'y': 'y_reconstructed',
                                     'z': 'z_reconstructed'},
                            inplace=True)
reconstructed_coords.set_index('cell_label', inplace=True)
reconstructed_coords.head(5)

reconstructed_coordinates.csv: 100%|███████████████████████████████████████████████████████████| 256M/256M [00:12<00:00, 20.4MMB/s]

	x_reconstructed	y_reconstructed	z_reconstructed	parcellation_index
cell_label
1019171911101460569	7.143894	7.890964	0.8	945
1019171911101550321	4.188673	7.962972	0.8	945
1019171911100841066	6.859447	5.908534	0.8	893
1019171911101400425	3.952014	7.564086	0.8	842
1019171911101380264	2.803546	7.221688	0.8	0

cell_joined = cell.join(reconstructed_coords, how='inner')
cell_joined.head(5)

	brain_section_label	cluster_alias	average_correlation_score	feature_matrix_label	donor_label	donor_genotype	donor_sex	x_section	y_section	z_section	...	cluster	neurotransmitter_color	class_color	subclass_color	supertype_color	cluster_color	x_reconstructed	y_reconstructed	z_reconstructed	parcellation_index
cell_label
1019171907102340387-1	C57BL6J-638850.37	1408	0.596276	C57BL6J-638850	C57BL6J-638850	wt/wt	M	7.226245	4.148963	6.6	...	0515 DG-PIR Ex IMN_2	#666666	#16f2f2	#3D53CC	#CC7A3D	#73FFBF	7.255606	4.007680	6.6	1160
1104095349101460194-1	C57BL6J-638850.26	4218	0.641180	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.064889	7.309543	4.2	...	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63	5.036436	7.264429	4.2	564
1017092617101450577	C57BL6J-638850.25	4218	0.763531	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.792921	8.189973	4.0	...	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63	5.784270	8.007646	4.0	761
1018093344101130233	C57BL6J-638850.13	4218	0.558073	C57BL6J-638850	C57BL6J-638850	wt/wt	M	3.195950	5.868655	2.4	...	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63	3.161528	5.719814	2.4	718
1019171912201610094	C57BL6J-638850.27	4218	0.591009	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.635732	7.995842	4.4	...	4199 PG-TRN-LRN Fat2 Glut_1	#2B93DF	#6b5ca5	#9B7ACC	#990041	#663D63	5.618763	7.847877	4.4	761

5 rows × 24 columns

We repeat the process for the cell CCF coordinates

ccf_coords = abc_cache.get_metadata_dataframe(
    directory='MERFISH-C57BL6J-638850-CCF',
    file_name='ccf_coordinates',
    dtype={"cell_label": str}
)
ccf_coords.rename(columns={'x': 'x_ccf',
                           'y': 'y_ccf',
                           'z': 'z_ccf'},
                  inplace=True)
ccf_coords.drop(['parcellation_index'], axis=1, inplace=True)
ccf_coords.set_index('cell_label', inplace=True)
ccf_coords.head(5)

ccf_coordinates.csv: 100%|█████████████████████████████████████████████████████████████████████| 296M/296M [00:13<00:00, 21.7MMB/s]

	x_ccf	y_ccf	z_ccf
cell_label
1019171911101460569	12.282330	6.987808	7.385773
1019171911101550321	12.192214	7.002155	4.366855
1019171911100841066	12.500341	4.750392	7.074634
1019171911101400425	12.231647	6.544816	4.128568
1019171911101380264	12.238502	6.135836	2.948194

cell_joined = cell_joined.join(ccf_coords, how='inner')
cell_joined.head(5)

	brain_section_label	cluster_alias	average_correlation_score	feature_matrix_label	donor_label	donor_genotype	donor_sex	x_section	y_section	z_section	...	subclass_color	supertype_color	cluster_color	x_reconstructed	y_reconstructed	z_reconstructed	parcellation_index	x_ccf	y_ccf	z_ccf
cell_label
1019171907102340387-1	C57BL6J-638850.37	1408	0.596276	C57BL6J-638850	C57BL6J-638850	wt/wt	M	7.226245	4.148963	6.6	...	#3D53CC	#CC7A3D	#73FFBF	7.255606	4.007680	6.6	1160	7.495417	2.445872	7.455066
1104095349101460194-1	C57BL6J-638850.26	4218	0.641180	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.064889	7.309543	4.2	...	#9B7ACC	#990041	#663D63	5.036436	7.264429	4.2	564	9.227966	6.133693	5.225024
1017092617101450577	C57BL6J-638850.25	4218	0.763531	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.792921	8.189973	4.0	...	#9B7ACC	#990041	#663D63	5.784270	8.007646	4.0	761	9.344912	6.989939	6.002664
1018093344101130233	C57BL6J-638850.13	4218	0.558073	C57BL6J-638850	C57BL6J-638850	wt/wt	M	3.195950	5.868655	2.4	...	#9B7ACC	#990041	#663D63	3.161528	5.719814	2.4	718	10.977068	4.398568	3.305223
1019171912201610094	C57BL6J-638850.27	4218	0.591009	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.635732	7.995842	4.4	...	#9B7ACC	#990041	#663D63	5.618763	7.847877	4.4	761	8.997138	6.798329	5.827197

5 rows × 27 columns

Visualize cells in section, reconstruced and CCF coordinates

We define a helper function to visualize cells in a section colorized by either cluster or annotation term and optionally with a boundary overlay

def plot_section(xx=None, yy=None, cc=None, val=None, pcmap=None, 
                 overlay=None, extent=None, bcmap=plt.cm.Greys_r, alpha=1.0,
                 fig_width = 6, fig_height = 6):
    
    fig, ax = plt.subplots()
    fig.set_size_inches(fig_width, fig_height)

    if xx is not None and yy is not None and pcmap is not None:
        plt.scatter(xx, yy, s=0.5, c=val, marker='.', cmap=pcmap)
    elif xx is not None and yy is not None and cc is not None:
        plt.scatter(xx, yy, s=0.5, color=cc, marker='.', zorder=1)   
        
    if overlay is not None and extent is not None and bcmap is not None:
        plt.imshow(overlay, cmap=bcmap, extent=extent, alpha=alpha, zorder=2)
        
    ax.set_ylim(11, 0)
    ax.set_xlim(0, 11)
    ax.axis('equal')
    ax.set_xticks([])
    ax.set_yticks([])
    
    return fig, ax

For example section “C57BL6J-638850.40”, we plot cells by section, reconstructed and ccf coordinates colorized by neurotransmitter type assignment. Note that cells from a single brain section remains together at the same z (posterior-to-anterior) value in section and reconstructed coordinates. Since the brain sections are at an angle compared to the CCF, the cells are spread across multiple x (anterior-to-posterior) CCF coordinates.

brain_section = 'C57BL6J-638850.40'
pred = (cell_joined['brain_section_label'] == brain_section)
section = cell_joined[pred]
print(len(section))

104572

print("z_section values:", np.unique(section['z_section']))
print("z_reconstructed values:", np.unique(section['z_reconstructed']))
print("x_ccf values:", np.unique(section['x_ccf']))

z_section values: [7.2]
z_reconstructed values: [7.2]
x_ccf values: [6.32598314 6.32656693 6.3266852  ... 7.16574148 7.16579943 7.16598123]

fig, ax = plot_section(xx=section['x_section'],
                       yy=section['y_section'], 
                       cc=section['neurotransmitter_color'])
res = ax.set_title("Neuortransmitter - Section Coordinates")
plt.show()

fig, ax = plot_section(xx=section['x_reconstructed'],
                       yy=section['y_reconstructed'], 
                       cc=section['neurotransmitter_color'])
res = ax.set_title("Neuortransmitter - Reconstructed Coordinates")
plt.show()

fig, ax = plot_section(xx=section['z_ccf'],
                       yy=section['y_ccf'], 
                       cc=section['neurotransmitter_color'])
res = ax.set_title("Neuortransmitter - CCF Coordinates")
plt.show()

Associate mapped parcellation and parcellation terms to the MERFISH data

Read in the pivoted parcellation annotation information we created in the Allen CCFv3 tutorial and join with the cell metadata dataframe.

parcellation_annotation = abc_cache.get_metadata_dataframe(directory='Allen-CCF-2020',
                                                           file_name='parcellation_to_parcellation_term_membership_acronym')
parcellation_annotation.set_index('parcellation_index', inplace=True)
parcellation_annotation.columns = ['parcellation_%s'% x for x in  parcellation_annotation.columns]
parcellation_annotation.head(5)

	parcellation_organ	parcellation_category	parcellation_division	parcellation_structure	parcellation_substructure
parcellation_index
0	unassigned	unassigned	unassigned	unassigned	unassigned
1	brain	grey	HY	TMv	TMv
2	brain	grey	Isocortex	SSp-m	SSp-m6b
5	brain	fiber tracts	lfbs	cst	int
6	brain	grey	P	PSV	PSV

parcellation_color = abc_cache.get_metadata_dataframe(directory='Allen-CCF-2020',
                                                      file_name='parcellation_to_parcellation_term_membership_color')
parcellation_color.set_index('parcellation_index', inplace=True)
parcellation_color.columns = ['parcellation_%s'% x for x in  parcellation_color.columns]
parcellation_color.head(5)

	parcellation_organ_color	parcellation_category_color	parcellation_division_color	parcellation_structure_color	parcellation_substructure_color
parcellation_index
0	#000000	#000000	#000000	#000000	#000000
1	#FFFFFF	#BFDAE3	#E64438	#FF4C3E	#FF4C3E
2	#FFFFFF	#BFDAE3	#70FF71	#188064	#188064
5	#FFFFFF	#CCCCCC	#CCCCCC	#CCCCCC	#CCCCCC
6	#FFFFFF	#BFDAE3	#FF9B88	#FFAE6F	#FFAE6F

cell_joined = cell_joined.join(parcellation_annotation, on='parcellation_index')
cell_joined = cell_joined.join(parcellation_color, on='parcellation_index')
cell_joined.head(5)

	brain_section_label	cluster_alias	average_correlation_score	feature_matrix_label	donor_label	donor_genotype	donor_sex	x_section	y_section	z_section	...	parcellation_organ	parcellation_category	parcellation_division	parcellation_structure	parcellation_substructure	parcellation_organ_color	parcellation_category_color	parcellation_division_color	parcellation_structure_color	parcellation_substructure_color
cell_label
1019171907102340387-1	C57BL6J-638850.37	1408	0.596276	C57BL6J-638850	C57BL6J-638850	wt/wt	M	7.226245	4.148963	6.6	...	brain	grey	HPF	DG	DG-po	#FFFFFF	#BFDAE3	#7ED04B	#7ED04B	#7ED04B
1104095349101460194-1	C57BL6J-638850.26	4218	0.641180	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.064889	7.309543	4.2	...	brain	grey	P	TRN	TRN	#FFFFFF	#BFDAE3	#FF9B88	#FFBA86	#FFBA86
1017092617101450577	C57BL6J-638850.25	4218	0.763531	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.792921	8.189973	4.0	...	brain	grey	P	P-unassigned	P-unassigned	#FFFFFF	#BFDAE3	#FF9B88	#FF9B88	#FF9B88
1018093344101130233	C57BL6J-638850.13	4218	0.558073	C57BL6J-638850	C57BL6J-638850	wt/wt	M	3.195950	5.868655	2.4	...	brain	fiber tracts	cbf	arb	arb	#FFFFFF	#CCCCCC	#CCCCCC	#CCCCCC	#CCCCCC
1019171912201610094	C57BL6J-638850.27	4218	0.591009	C57BL6J-638850	C57BL6J-638850	wt/wt	M	5.635732	7.995842	4.4	...	brain	grey	P	P-unassigned	P-unassigned	#FFFFFF	#BFDAE3	#FF9B88	#FF9B88	#FF9B88

5 rows × 37 columns

The joined DataFrame cell_joined created above is available from the cache as the file cell_metadata_with_parcellation_annotation.

Visualize cell parcellation assignment at different anatomical levels

For example section “C57BL6J-638850.40”, we plot cells reconstructed coordinates colorized by parcellation assignment at the division and structure anatomical levels

pred = (cell_joined['brain_section_label'] == brain_section)
section = cell_joined[pred]
print(len(section))

104572

fig, ax = plot_section(xx=section['x_reconstructed'],
                       yy=section['y_reconstructed'], 
                       cc=section['parcellation_division_color'])
res = ax.set_title("Division - Reconstructed Coordinates")
plt.show()

fig, ax = plot_section(xx=section['x_reconstructed'],
                       yy=section['y_reconstructed'], 
                       cc=section['parcellation_structure_color'])
res = ax.set_title("Structure - Reconstructed Coordinates")
plt.show()

fig, ax = plot_section(xx=section['x_reconstructed'],
                       yy=section['y_reconstructed'], 
                       cc=section['parcellation_substructure_color'])
res = ax.set_title("Substructure - Reconstructed Coordinates")
plt.show()

Aggregate cells by parcellation assignment at different anatomical levels

Define a helper function to count the number of cells for each term and apply it to the division and structure anatomical levels

def parcellation_cell_count (df,level) :
    cell_count = df.groupby(level)[['cluster_alias']].count()
    cell_count.columns = ['number_of_cells']
    cell_count.sort_values('number_of_cells', ascending=False, inplace=True)
    return cell_count

cell_count = parcellation_cell_count(cell_joined, 'parcellation_division')
cell_count.head(10)

	number_of_cells
parcellation_division
Isocortex	935742
STR	401346
CB	383127
HPF	304642
MB	281852
OLF	274354
MY	147562
P	136569
TH	133805
HY	132902

cell_count = parcellation_cell_count(section, 'parcellation_structure')
cell_count.head(10)

	number_of_cells
parcellation_structure
SSp-bfd	8865
SSs	7210
CP	4715
DG	4491
VISa	4462
CA1	3705
PIR	3119
cst	3000
cc	2858
RSPv	2853

We can filtered cells to specific subclasses and types and count within the filtered set

pred = (cell_joined['subclass'] == '145 MH Tac2 Glut')
filtered = cell_joined[pred]
print("number of cells:", len(filtered))
cell_count = parcellation_cell_count(filtered, 'parcellation_structure')
cell_count.head(10)

number of cells: 4696

	number_of_cells
parcellation_structure
MH	4404
V3-unassigned	132
mfsbshy	73
brain-unassigned	46
chpl	21
LH	12
DG	3
TH-unassigned	2
LP	1
PO	1

pred = (cell_joined['subclass'] == '146 LH Pou4f1 Sox1 Glut')
filtered = cell_joined[pred]
print("number of cells:", len(filtered))
cell_count = parcellation_cell_count(filtered, 'parcellation_structure')
cell_count.head(10)

number of cells: 1067

	number_of_cells
parcellation_structure
LH	851
mfsbshy	109
MH	59
PAG	10
TH-unassigned	10
brain-unassigned	7
PF	6
DG	5
MD	4
SPA	2

pred = (cell_joined['subclass'] == '147 AD Serpinb7 Glut')
filtered = cell_joined[pred]
print("number of cells:", len(filtered))
cell_count = parcellation_cell_count(filtered, 'parcellation_structure')
cell_count.head(10)

number of cells: 1190

	number_of_cells
parcellation_structure
AD	1036
mfsbshy	84
brain-unassigned	35
DG	22
LD	4
AM	3
TH-unassigned	3
mfbc	3

pred = (cell_joined['subclass'] == '007 L2/3 IT CTX Glut')
filtered = cell_joined[pred]
print("number of cells:", len(filtered))
cell_count = parcellation_cell_count(filtered, 'parcellation_substructure')
cell_count.head(10)

number of cells: 121784

	number_of_cells
parcellation_substructure
MOs2/3	12762
MOp2/3	10586
VISp2/3	9250
SSs2/3	7149
SSp-m2/3	5203
SSp-bfd2/3	5032
TEa2/3	4396
SSp-ul2/3	3337
RSPagl2/3	3142
ACAv2/3	3085

Resampled CCF template and parcellations

The anatomical template, parcellation and boundary volume in resample CCF coordinates are also available for download for visualization and analysis

abc_cache.list_data_files('MERFISH-C57BL6J-638850-CCF')

['resampled_annotation',
 'resampled_annotation_boundary',
 'resampled_average_template']

print("reading resampled_average_template")
file = abc_cache.get_data_path(directory='MERFISH-C57BL6J-638850-CCF',
                               file_name='resampled_average_template')
average_template_image = sitk.ReadImage(file)
average_template_array = sitk.GetArrayViewFromImage(average_template_image)

print("reading resampled_annotation")
file = abc_cache.get_data_path(directory='MERFISH-C57BL6J-638850-CCF',
                               file_name='resampled_annotation')
annotation_image = sitk.ReadImage(file)
annotation_array = sitk.GetArrayViewFromImage(annotation_image)

print("reading resampled_annotation_boundary")
file = abc_cache.get_data_path(directory='MERFISH-C57BL6J-638850-CCF',
                               file_name='resampled_annotation_boundary')
annotation_boundary_image = sitk.ReadImage(file)
annotation_boundary_array = sitk.GetArrayViewFromImage(annotation_boundary_image)

reading resampled_average_template

resampled_average_template.nii.gz: 100%|███████████████████████████████████████████████████████| 117M/117M [00:05<00:00, 19.9MMB/s]

reading resampled_annotation

resampled_annotation.nii.gz: 100%|███████████████████████████████████████████████████████████| 2.12M/2.12M [00:00<00:00, 11.0MMB/s]

reading resampled_annotation_boundary

resampled_annotation_boundary.nii.gz: 100%|██████████████████████████████████████████████████| 1.55M/1.55M [00:00<00:00, 7.87MMB/s]

# Function to print out image information
def image_info(img):
    print('size: ' + str(img.GetSize()) + ' voxels')
    print('spacing: ' + str(img.GetSpacing()) + ' mm' )
    print('direction: ' + str(img.GetDirection()) )
    print('origin: ' + str(img.GetOrigin()))

image_info(average_template_image)

size: (1100, 1100, 76) voxels
spacing: (0.009999999776482582, 0.009999999776482582, 0.20000000298023224) mm
direction: (-1.0, 0.0, 0.0, 0.0, -0.0, -1.0, 0.0, -1.0, 0.0)
origin: (5.476531505584717, 8.600000381469727, 6.435669422149658)

To enable the overlay of image section with cell coordinates we need to compute the extent the image in mm coordinates

size = average_template_image.GetSize()
spacing = average_template_image.GetSpacing()
extent = (-0.5 * spacing[0], (size[0]-0.5) * spacing[0], (size[1]-0.5) * spacing[1], -0.5 * spacing[1])

Visualize cells with parcellation boundary overlay

For example section “C57BL6J-638850.40”, we first compute the corresponding z section in the resampled image volume and visualize the resampled average template, annotation and parcellation for that section

zindex = int(section.iloc[0]['z_reconstructed'] / 0.2)
zindex

36

template_slice = average_template_array[zindex, :, :]
fig, ax = plot_section(overlay=template_slice, extent=extent)
res = ax.set_title('resampled_average_template')
plt.show()

annotation_slice = annotation_array[zindex, :, :]
fig, ax = plot_section(overlay=annotation_slice, extent=extent)
res = ax.set_title('resampled_annotation')
plt.show()

boundary_slice = annotation_boundary_array[zindex, :, :]
fig, ax = plot_section(overlay=boundary_slice, bcmap=plt.cm.Greys, extent=extent)
res = ax.set_title('resampled_annotation_boundary')
plt.show()

Using the help function, we can overlay the parcellation boundary overlay on top of the cells colorized by subclass

fig, ax = plot_section(section['x_reconstructed'],
                       section['y_reconstructed'], 
                       cc=section['subclass_color'],
                       overlay=boundary_slice,
                       extent=extent, 
                       bcmap=plt.cm.Greys,
                       alpha = 1.0*(boundary_slice>0),
                       fig_width = 9,
                       fig_height = 9 )
res = ax.set_title("Subclass - Reconstructed Coordinates")
plt.show()