From 926c3afe1bf7306a545d40e0b91fff9421212dfc Mon Sep 17 00:00:00 2001 From: Philipp Schlegel Date: Wed, 25 Sep 2024 19:55:42 +0100 Subject: [PATCH] update tutorials: - add new tutorial on skeletonizing light-level data - update cross-links - exclude all `zzz` tutorials from testing - small fixes for cortex tutorial --- docs/_static/lm_tut/all_skeletons.png | Bin 0 -> 964663 bytes docs/_static/lm_tut/download.png | Bin 0 -> 364175 bytes docs/_static/lm_tut/image_stack.png | Bin 0 -> 2195878 bytes docs/_static/lm_tut/labels.png | Bin 0 -> 1019980 bytes docs/_static/lm_tut/mask.png | Bin 0 -> 879301 bytes docs/_static/lm_tut/stack.gif | Bin 0 -> 1397894 bytes docs/_static/lm_tut/z_stack.png | Bin 0 -> 2407704 bytes docs/_static/lm_tut/zoom_in.png | Bin 0 -> 1753183 bytes .../examples/0_io/tutorial_io_00_skeletons.py | 3 + .../0_io/zzz_tutorial_io_05_skeletonize.py | 234 ++++++++++++++++++ .../1_plotting/tutorial_plotting_06_cortex.py | 8 +- tests/test_tutorials.py | 4 +- 12 files 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zu=4=!c$Rqxbac{fm&v?I&z+^nSpa8dK0;|K z9>5)pIsQEABCi+UVc)FoJtU!Jn}h5%d+umI+i4-3mH9~J0E&`@sz}~TAvr4hP&Di6 zeE;VFtJq;&1Jyv|b2Zn2_iCGZ7R%!Lmgy_TJ*S`l9p}5un@E=(kNWWYY3xJ5LXCm2 z=~OfpRg|zqi^blRfdX;^j9dh1c1)@sF=a1~c)I z6F5E+8T^ydtdTBKQ(rD?*BdqQTokt?K(L&#PeQZ0HMg`siFglopY1p0kFC_C$xDX* zy#)WCxP)ts@`8;q)60SLe`@60mw6kg)=#y2N0gMFzbPYA8n(7GqKe`57oOtjTH}=y z%QI!<`d`JG+WEsH^@nteb4k9{$h7x(0V!#n6wG+7M2Y6D};!&Z2LEuYN#c5Nk}PNuihOVHeZnoK0%X#QXZo}9j7-eoxE()EnQ zRkz)>V;@aczOdhU{pGb{CO11)G2%sP-Oui0WXid*u%>WycFC5!mBR{eYegfQ*~S+n zfM>^6Sou{3Pp12A{?kjg+<+pd5jER6$4H}X?r2x=0kDSsbBzw6>YBU;`M+MH^y(v* xf3F(}^DfWczHF*N#Q*>C|Lt?QXnUEMb#g+efsG@Lz5@S#+_7(a=C "Colors" -> "Split Channels" to split the image into the channels +4. Discard all but the red "C1" channel with our neurons +5. Go to "Image" -> "Type" -> "8-bit" to convert the image to 8-bit (optional but recommended) +6. Save via "File" -> "Save As" -> "NRRD" and save the file as `neuron.nrrd` + +![Z stack](../../../_static/lm_tut/image_stack.png) + +## Extracting the Skeleton + +Now that we have that file in a format we can load it into Python, we can get started: +""" + +# %% +import kimimaro +import nrrd +import navis +import cc3d +import numpy as np + +# %% +# First load the image stack: + +# `im` is numpy array, `header` is a dictionary +im, header = nrrd.read( + "neuron.nrrd" +) + +# Next, we need to find some sensible threshold to binarize the image. This is not strictly +# necessary (see the further note down) but at least for starters this more intuitive. + +# Threshold the image +mask = (im >= 20).astype(np.uint8) + +# %% +# You can inspect the mask to see if the thresholding worked as expected: +# ```python +# import matplotlib.pyplot as plt +# plt.imshow(mask.max(axis=2)) +# ``` +# +# With the `octarine` backend, you can also visualize the volume in 3d: +# ```python +# # spacing can be found in the `header` dictionary +# import octarine as oc +# v = oc.Viewer() +# v.add_volume(mask, spacing=(.19, .19, .38)) +# ``` +# +# ![mask](../../../_static/lm_tut/mask.png) +# +# A couple notes on the thresholding: +# +# - feel free to test the right threshold in e.g. ImageJ/Fiji +# - remove as much background as possible without disconnecting neurites +# - perfection is the enemy of progress: we can denoise/reconnect during postprocessing +# +# Next, we we need to label the connected components in the image: + +# %% +# Extract the labels +labels, N = cc3d.connected_components(mask, return_N=True) + +# %% +# Visualize the labels: +# ```python +# import cmap +# import octarine as oc +# v = oc.Viewer() +# v.add_volume(labels, spacing=(.19, .19, .38), color=cmap.Colormap('prism')) +# ``` +# +# ![labels](../../../_static/lm_tut/labels.png) +# +# !!! experiment +# `cc3d.connected_component` also works with non-thresholded image - see the `delta` parameter. + +# Collect some statistics +stats = cc3d.statistics(labels) + +print("Labeled", N, "connected components") +print("Per-label voxel counts:", stats["voxel_counts"]) + +# %% +# Note how the first label has suspiciously many voxels? That's because this is the background label. +# We need to make sure to exlude it from the skeletonization process: +to_skeletonize = np.arange(1, N) + + +# %% +# Now we can run the actual skeletonization. There are a number of parameters that are worth tweaking: +# +# - `scale` & `const` control how detailed your skeleton will be: lower = more detailed but more noise +# - `anisotropy` controls the voxel size - see the `header` dictionary for the voxel size of our image +# - `dust_threshold` controls how small connected components are skipped +# - `object_ids` is a list of labels to process (remember that we skipped the background label) +# - `max_path` if this is set, the algorithm will only process N paths in each skeleton - you can use +# this to finish early (e.g. for testing) +# +# See the [`kimimaro` repository](https://github.com/seung-lab/kimimaro) for a detailed explanation +# of the parameters! + +skels = kimimaro.skeletonize( + labels, + teasar_params={ + "scale": 1.5, + "const": 1, # physical units (1 micron in our case) + "pdrf_scale": 100000, + "pdrf_exponent": 4, + "soma_acceptance_threshold": 3.5, # physical units + "soma_detection_threshold": 1, # physical units + "soma_invalidation_const": 0.5, # physical units + "soma_invalidation_scale": 2, + "max_paths": None, # default None + }, + object_ids=list(to_skeletonize), # process only the specified labels + dust_threshold=500, # skip connected components with fewer than this many voxels + anisotropy=(0.19, .19, 0.38), # voxel size in physical units + progress=True, # show progress bar + parallel=6, # <= 0 all cpu, 1 single process, 2+ multiprocess + parallel_chunk_size=1, # how many skeletons to process before updating progress bar +) + +# %% +# `skels` is a dictionary of `{label: cloudvolume.Skeleton}`. Let's convert these to {{ navis }} neurons: + +# Convert skeletons to NAVis neurons +nl = navis.NeuronList([navis.read_swc(s.to_swc(), id=i) for i, s in skels.items()]) + +# %% +# Based on the voxel sizes in the `stats`, we can make an educated guess that label `6423` is one of our neurons. +# Let's visualize it in 3D: +# +# ```python +# import octarine as oc +# v = oc.Viewer() +# v.add_neurons(nl.idx[6423], color='r', linewidth=2, radius=False)) +# v.add_volume(im, spacing=(.19, .19, .38), opacity=.5) +# ``` +# +# ![stack animation](../../../_static/lm_tut/stack.gif) +# +# !!! important +# The above image is supposed to be a GIF animation toggling the neuron on and off. +# If you see just a static image, try reload the page. +# +# This looks pretty good off the bat! Now obviously we will have the other larger neuron (label `6091`) +# plus bunch of smaller skeletons in our NeuronList: +# +# ![all skeletons](../../../_static/lm_tut/all_skeletons.png) +# +# Zooming in on `6091` you will see that it wasn't fully skeletonized: some of the branches are missing +# and others are disconnected. That's either because our threshold for the mask was too high (this neuron +# was fainter than the other) and/or we dropped too many fragments during the skeletonization process +# (see the `dust_threshold` parameter). +# +# ![zoom in](../../../_static/lm_tut/zoom_in.png) +# +# ## Acknowledgements +# +# The packages we used here were written by the excellent Will Silversmith from the Seung lab in Princeton. +# The image stack we processed is from the Janelia Split-Gal4 collection and was published as part of the +# [Cheong, Eichler, Stuerner, _et al._ (2024)](https://elifesciences.org/reviewed-preprints/96084v1) paper. +# +# ## Alternatives +# +# If the method described in this tutorial does not work for you, there are a number of alternatives: +# +# 1. [Simple Neurite Tracer](https://imagej.net/plugins/snt/index) is a popular ImageJ plugin for semi-automated tracing +# 2. Folks at the Allen Institute for Brain Science have published a [protocol for reconstructing neurons](https://portal.brain-map.org/explore/toolkit/morpho-reconstruction/vaa3d-mozak) +# 3. [NeuTube](https://neutracing.com/tutorial/) is an open-source software for reconstructing neurongs from fluorescence microscopy images + +# %% + +# mkdocs_gallery_thumbnail_path = '_static/lm_tut/z_stack.png' \ No newline at end of file diff --git a/docs/examples/1_plotting/tutorial_plotting_06_cortex.py b/docs/examples/1_plotting/tutorial_plotting_06_cortex.py index 93ceed72..f94d1fed 100644 --- a/docs/examples/1_plotting/tutorial_plotting_06_cortex.py +++ b/docs/examples/1_plotting/tutorial_plotting_06_cortex.py @@ -63,7 +63,7 @@ # %% # The physical soma depth is simply the normalized depth multiplied by the total depth of the cortex. # Note that we're positioning from the bottom - i.e. 922.586 will be at the surface and 0 at the bottom! -# This is to make our lifes easier when it comes to plotting: the origin in `matplotlib` +# This is to make our lifes easier when it comes to plotting since the origin in `matplotlib` # figures is in the bottom left corner. phys_y = (1 - n.cell_soma_normalized_depth) * 922.5861720311 @@ -191,7 +191,7 @@ def plot_neurons(to_plot, color="purple", axon_color="magenta", offset=500): # %% [markdown] # That looks close enough. The last bit is to add the little KDE plots for the depth-distribution of -# cable length: +# cable length! # # We're going to be cheap here and simply generate a histogram over the node positions. # To make this representative, we should make sure that the number of nodes per unit of cable @@ -232,11 +232,11 @@ def plot_neurons(to_plot, color="purple", axon_color="magenta", offset=500): # Add histograms # For axon: sns.kdeplot( - data=nodes[nodes.label == 2], y="y", ax=ax_hist, color="purple", linewidth=1.5 + data=nodes[nodes.label == 2], y="y", ax=ax_hist, color="magenta", linewidth=1.5 ) # For the rest: sns.kdeplot( - data=nodes[nodes.label != 2], y="y", ax=ax_hist, color="magenta", linewidth=1.5 + data=nodes[nodes.label != 2], y="y", ax=ax_hist, color="purple", linewidth=1.5 ) # Add soma positions diff --git a/tests/test_tutorials.py b/tests/test_tutorials.py index 3954feec..3b70984b 100644 --- a/tests/test_tutorials.py +++ b/tests/test_tutorials.py @@ -21,8 +21,6 @@ from pathlib import Path from contextlib import contextmanager -SKIP = ["zzz_tutorial_nblast_01_flycircuit.py", "zzz_tutorial_nblast_02_hemibrain.py"] - @contextmanager def suppress_stdout(): @@ -52,7 +50,7 @@ def suppress_stdout(): for i, file in enumerate(files): if not file.is_file(): continue - if file.name in SKIP: + if file.name.startswith('zzz'): continue # Note: we're using `exec` here instead of e.g. `subprcoess.run` because we need to avoid

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