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DOC: Update LabelMaps example
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‎README.rst‎

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@@ -78,6 +78,7 @@ Interactive Jupyter_ widgets to visualize images, point sets, and meshes.
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- Exquisite volume rendering
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- Tri-plane volume slicing
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- Innovative, powerful opacity transfer function / window / level widget
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- Label map segmentation 2D and 3D rendering
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- Anisotropic voxel spacing supported
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- Image line profile widget
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- Compare images widget
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- `Binder: 2D ITK Images <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples%2F2DImage.ipynb>`_
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- `Binder: 3D ITK Images <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples%2F3DImage.ipynb>`_
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- `Binder: 3D Label Maps <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples%2FLabelMaps.ipynb>`_
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- `Binder: Dask Array images <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples/DaskArray.ipynb>`_
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- `Binder: Large volumes <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples/LargeVolumes.ipynb>`_
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- `Binder: NumPy array images (processed with SciPy) <https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/master?urlpath=lab/tree/examples/NumPyArrayImage.ipynb>`_
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- `2D ITK Images <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/2DImage.ipynb>`_
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- `3D ITK Images <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/3DImage.ipynb>`_
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- `3D Label maps <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/LabelMaps.ipynb>`_
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- `Dask Array images <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/DaskArray.ipynb>`_
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- `Large volumes <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/LargeVolumes.ipynb>`_
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- `ImageJ ImgLib2 images <https://github.com/InsightSoftwareConsortium/itkwidgets/blob/master/examples/ImageJImgLib2.ipynb>`_ (requires `conda <https://conda.io/>`_ and a local `Fiji <https://fiji.sc/>`_ installation)

‎examples/LabelMap.ipynb‎

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‎examples/LabelMaps.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install dependencies for this example\n",
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"# Note: This does not include itkwidgets, itself\n",
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"import sys\n",
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"!{sys.executable} -m pip install -U itk itk-totalvariation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from urllib.request import urlretrieve\n",
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"import os\n",
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"\n",
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"import itk\n",
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"import numpy as np\n",
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"\n",
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"from itkwidgets import view\n",
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"import itkwidgets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Download data\n",
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"# Source: https://data.broadinstitute.org/bbbc/BBBC024/\n",
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"file_name = 'HL50_cell_line_c00_03_extraction.tif'\n",
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"if not os.path.exists(file_name):\n",
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" url = 'https://data.kitware.com/api/v1/file/5b61f16c8d777f06857c1949/download'\n",
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" urlretrieve(url, file_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "530d6f97e04e4dab86bbb00ddcc799bb",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Viewer(annotations=False, cmap=['BrBG'], geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"image = itk.imread(file_name, itk.F)\n",
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"\n",
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"view(image, cmap=itkwidgets.cm.BrBG, annotations=False, vmax=800, ui_collapsed=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Segment the cells\n",
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"smoothed = itk.prox_tv_image_filter(image, weights=50, maximum_number_of_iterations=3)\n",
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"\n",
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"LabelMapType = itk.Image[itk.UC, 3]\n",
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"\n",
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"threshold_filter = itk.MomentsThresholdImageFilter[type(smoothed), LabelMapType].New(smoothed)\n",
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"threshold_filter.SetInsideValue(0)\n",
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"threshold_filter.SetOutsideValue(1)\n",
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"threshold_filter.Update()\n",
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"threshold = threshold_filter.GetThreshold()\n",
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"thresholded = threshold_filter.GetOutput()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "69dfe9c8df3d41a889e9c7b63f114284",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Viewer(geometries=[], gradient_opacity=0.22, interpolation=False, point_sets=[], rendered_label_map=<itk.itkIm…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"view(label_map=thresholded)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "33c0cb1ad92241a49728735b67d1f83f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Viewer(geometries=[], gradient_opacity=0.22, interpolation=False, point_sets=[], rendered_label_map=<itk.itkIm…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"connected_components = itk.connected_component_image_filter(thresholded)\n",
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"\n",
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"view(label_map=connected_components)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "94c8055aff1a4cb89923c78072fa305b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Viewer(geometries=[], gradient_opacity=0.5, interpolation=False, label_map_names=[(0, 'Background'), (1, 'Firs…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# We can combine the label map with the intensity image, add names for the labels, etc.\n",
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"names = [(0, 'Background'), (1, 'First cell'), (2, 'Second cell')]\n",
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"viewer = view(smoothed,\n",
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" label_map=connected_components,\n",
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" label_map_names=names,\n",
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" label_map_blend=0.8,\n",
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" rotate=True,\n",
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" gradient_opacity=0.5,\n",
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" slicing_planes=True)\n",
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"viewer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Clicking on a label in a slice will toggle its weight.\n",
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"We can also set label weights programmically."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"label_count = len(np.unique(connected_components))\n",
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"label_weights = np.ones((label_count,), dtype=np.float32)\n",
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"label_weights[1] = 0.1\n",
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"label_weights[2] = 0.2\n",
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"viewer.label_map_weights = label_weights"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also combine the intensity images, weighted and blended with the label maps, with iso-surface geometry."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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