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Merged
merged 3 commits into from
Sep 17, 2020
Merged

21 add DICOM format in the LoadImage tutorial #23

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34570fa
[DLMED] add DICOM format in the tutorial
Nic-Ma Sep 16, 2020
20186d8
[DLMED] update according to comments
Nic-Ma Sep 16, 2020
0965147
[DLMED] update ITK shape
Nic-Ma Sep 17, 2020
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160 changes: 111 additions & 49 deletions load_medical_images.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -66,33 +66,11 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MONAI version: 0.2.0\n",
"Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]\n",
"Numpy version: 1.18.1\n",
"Pytorch version: 1.6.0\n",
"\n",
"Optional dependencies:\n",
"Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
"Nibabel version: 3.1.1\n",
"scikit-image version: 0.15.0\n",
"Pillow version: 7.2.0\n",
"Tensorboard version: 2.1.0\n",
"\n",
"For details about installing the optional dependencies, please visit:\n",
" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
"\n"
]
}
],
"outputs": [],
"source": [
"# Copyright 2020 MONAI Consortium\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
Expand Down Expand Up @@ -133,14 +111,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load image with default image reader\n",
"## Load Nifti image with default image reader\n",
"MONAI leverages `ITK` as the default image reader, it can support most of the common medical image formats.\n",
"More details, please check: https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 2,
"metadata": {
"tags": []
},
Expand All @@ -149,23 +127,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(128, 128, 128)\n",
"meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '128', 'dim[2]': '128', 'dim[3]': '128', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
"image data shape:(64, 128, 96)\n",
"meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '96', 'dim[2]': '128', 'dim[3]': '64', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n"
" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.nii.gz'}\n"
]
}
],
"source": [
"# generate 3D test images\n",
"tempdir = tempfile.mkdtemp()\n",
"test_image = np.random.rand(128, 128, 128)\n",
"test_image = np.random.rand(64, 128, 96)\n",
"filename = os.path.join(tempdir, \"test_image.nii.gz\")\n",
"itk_np_view = itk.image_view_from_array(test_image)\n",
"itk.imwrite(itk_np_view, filename)\n",
Expand All @@ -179,31 +157,31 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a list of images and stack as 1 training item\n",
"## Load a list of Nifti images and stack as 1 training item\n",
"Loading a list of files, stack them together and add a new dimension as first dimension.\n",
"\n",
"And use the meta data of the first image to represent the stacked result."
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(3, 128, 128, 128)\n",
"meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '128', 'dim[2]': '128', 'dim[3]': '128', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
"image data shape:(3, 64, 128, 96)\n",
"meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '96', 'dim[2]': '128', 'dim[3]': '64', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n"
" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.nii.gz'}\n"
]
}
],
Expand All @@ -219,6 +197,90 @@
"print(f\"meta data:{meta}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load 3D image in DICOM format"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(64, 128, 96)\n",
"meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.64732409224407717340688313759116819', '0008|0020': '20200917', '0008|0030': '012951.461675 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.22199909824554379284164927198357157', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.64330868579047092348166172965815757', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.36887462277578362563842268362959985', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.dcm'}\n"
]
}
],
"source": [
"filename = os.path.join(tempdir, \"test_image.dcm\")\n",
"dcm_image = np.random.randint(256, size=(64, 128, 96)).astype(np.uint8())\n",
"itk_np_view = itk.image_view_from_array(dcm_image)\n",
"itk.imwrite(itk_np_view, filename)\n",
"data, meta = LoadImage()(filename)\n",
"\n",
"print(f\"image data shape:{data.shape}\")\n",
"print(f\"meta data:{meta}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a list of DICOM images and stack as 1 training item\n",
"Loading a list of files, stack them together and add a new dimension as first dimension.\n",
"\n",
"And use the meta data of the first image to represent the stacked result."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(3, 64, 128, 96)\n",
"meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.55124182629917658983046437541692429', '0008|0020': '20200917', '0008|0030': '012953.377970 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.54570928137383298712421968711546946', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.79105954593572939973262028207196688', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.37569814707528773722727172695078933', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.dcm'}\n"
]
}
],
"source": [
"filenames = [\"test_image.dcm\", \"test_image2.dcm\", \"test_image3.dcm\"]\n",
"for i, name in enumerate(filenames):\n",
" filenames[i] = os.path.join(tempdir, name)\n",
" itk_np_view = itk.image_view_from_array(dcm_image)\n",
" itk.imwrite(itk_np_view, filenames[i])\n",
"data, meta = LoadImage()(filenames)\n",
"\n",
"print(f\"image data shape:{data.shape}\")\n",
"print(f\"meta data:{meta}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -228,7 +290,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 6,
"metadata": {
"tags": []
},
Expand All @@ -237,18 +299,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(256, 256)\n",
"image data shape:(128, 256)\n",
"meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
" [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
]
}
],
"source": [
"test_image = np.random.randint(0, 256, size=[256, 256])\n",
"test_image = np.random.randint(0, 256, size=[128, 256])\n",
"filename = os.path.join(tempdir, \"test_image.png\")\n",
"Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n",
"data, meta = LoadImage()(filename)\n",
Expand All @@ -272,26 +334,26 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(256, 256)\n",
"image data shape:(128, 256)\n",
"meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
" [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
]
}
],
"source": [
"loader = LoadImage()\n",
"loader.register(ITKReader(c_order_axis_indexing=True))\n",
"loader.register(ITKReader())\n",
"data, meta = loader(filename)\n",
"\n",
"print(f\"image data shape:{data.shape}\")\n",
Expand All @@ -310,15 +372,15 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image data shape:(256, 256, 2)\n",
"meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 256, 'info': {}, 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
"image data shape:(128, 256, 2)\n",
"meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 128, 'info': {}, 'spatial_shape': [256, 128], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
]
}
],
Expand All @@ -340,7 +402,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 9,
"metadata": {
"tags": []
},
Expand All @@ -355,7 +417,7 @@
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
]
}
],
Expand Down Expand Up @@ -383,7 +445,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand Down