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Merged
merged 3 commits into from
May 30, 2023
Merged

ENH: Add/update notebooks showing the pixel type support #224

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646ffc3
ENH: Bump itk-elastix version in requirements.txt
ntatsisk May 25, 2023
6d385d0
ENH: Add notebook that discusses pixel types
ntatsisk May 25, 2023
0a60fc5
ENH: Read masks as uchar in notebook
ntatsisk May 25, 2023
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2 changes: 1 addition & 1 deletion .binder/requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
itk-elastix>=0.12.0
itk-elastix>=0.17.1
itkwidgets>=0.32.0
jupyterlab>=2.2.0
imageio
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90 changes: 70 additions & 20 deletions examples/ITK_Example09_PointSetAndMaskTransformation.ipynb
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Original file line number Diff line number Diff line change
@@ -1,13 +1,15 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Point set and Mask Transformation"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -19,6 +21,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand Down Expand Up @@ -57,6 +60,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -65,7 +69,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -77,6 +81,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -85,25 +90,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Import Mask\n",
"moving_mask = itk.imread('data/CT_3D_lung_moving_mask.mha', itk.F)"
"# Note: Version v0.17.0 and onwards supports various image types for elastix and transformix functions/objects\n",
"moving_mask = itk.imread('data/CT_3D_lung_moving_mask.mha', itk.UC)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Mask image is a binary image, therefore the FinalBSplineInterpolationOrder should be 0\n",
"result_transform_parameters.SetParameter('FinalBSplineInterpolationOrder','0')\n"
"result_transform_parameters.SetParameter('FinalBSplineInterpolationOrder','0')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -112,7 +119,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -123,6 +130,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -131,7 +139,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -148,13 +156,15 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segmentation Transformation Evaluation"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -163,7 +173,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -180,12 +190,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Import groundtruth segmentation\n",
"fixed_mask = itk.imread('data/CT_3D_lung_fixed_mask.mha', itk.F)\n",
"fixed_mask = itk.imread('data/CT_3D_lung_fixed_mask.mha', itk.UC)\n",
"\n",
"# Cast itk images to numpy arrays and round result image to boolean array.\n",
"fixed_mask_np = np.asarray(fixed_mask)\n",
Expand All @@ -194,23 +204,47 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dice loss: 0.9862246731348647\n"
]
}
],
"source": [
"print(\"Dice loss:\",dice_loss(fixed_mask_np, result_mask_np))"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c76e48b027c84e37963cf1e02ee8a702",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"AppLayout(children=(HBox(children=(Label(value='Link:'), Checkbox(value=False, description='cmap'), Checkbox(v…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare(fixed_mask, result_moving_mask)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -219,7 +253,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -231,6 +265,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -239,7 +274,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -262,17 +297,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a044dd051e04f2f92d3dae77639590e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_set_colors=array([[0.8392157, 0. , 0. ]], dtype…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view(moving_image, point_sets=[result_point_set])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -286,7 +336,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.0"
"version": "3.9.0"
}
},
"nbformat": 4,
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