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zH;?lT|ykuUj^Px+N^`InFRnXmbq&-tD2 k`JWH^p)dNQPx_^A`lpZjsjvF0&-$(J`mYcBQ6CThJB#|e)c^nh diff --git a/docs/examples/0_io/zzz_tutorial_io_05_skeletonize.py b/docs/examples/0_io/zzz_tutorial_io_05_skeletonize.py index 86649c27..fa8263e7 100644 --- a/docs/examples/0_io/zzz_tutorial_io_05_skeletonize.py +++ b/docs/examples/0_io/zzz_tutorial_io_05_skeletonize.py @@ -35,9 +35,9 @@ ## The Data -The pipeline we're showing here was written for pre-segmented data, i.e. there is little in the way +The pipeline we're using here was written for pre-segmented data, i.e. there is little in the way of dealing with noisy data. There is of course nothing stopping you from doing some additional -pre-processing to clean up you data. +pre-processing to clean up you data _before_ running this pipeline. ### Download Image Stack @@ -98,7 +98,7 @@ # plt.imshow(mask.max(axis=2)) # ``` # -# With the `octarine` backend, you can also visualize the volume in 3d: +# 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 @@ -110,7 +110,7 @@ # # A couple notes on the thresholding: # -# - feel free to test the right threshold in e.g. ImageJ/Fiji +# - feel free to test the thresholding 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 # @@ -187,7 +187,7 @@ 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. +# Based on the voxel sizes in `stats`, we can make an educated guess that label `6423` is one of our neurons. # Let's visualize it in 3D: # # ```python @@ -199,12 +199,8 @@ # # ![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: +# This looks pretty good off the bat! Now obviously we will have the other large neuron (label `6091`) +# plus bunch of smaller skeletons in our NeuronList. Let's have a look at those as well: # # ![all skeletons](../../../_static/lm_tut/all_skeletons.png) # @@ -223,7 +219,7 @@ # # ## Alternatives # -# If the method described in this tutorial does not work for you, there are a number of alternatives: +# If the pipeline 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)

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