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21 add DICOM format in the LoadImage tutorial (#23)
* [DLMED] add DICOM format in the tutorial Signed-off-by: Nic Ma <[email protected]> * [DLMED] update according to comments Signed-off-by: Nic Ma <[email protected]> * [DLMED] update ITK shape Signed-off-by: Nic Ma <[email protected]>
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‎load_medical_images.ipynb

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@@ -66,33 +66,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MONAI version: 0.2.0\n",
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"Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]\n",
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"Numpy version: 1.18.1\n",
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"Pytorch version: 1.6.0\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
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"Nibabel version: 3.1.1\n",
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"scikit-image version: 0.15.0\n",
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"Pillow version: 7.2.0\n",
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"Tensorboard version: 2.1.0\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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"\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Copyright 2020 MONAI Consortium\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
@@ -133,14 +111,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load image with default image reader\n",
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"## Load Nifti image with default image reader\n",
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"MONAI leverages `ITK` as the default image reader, it can support most of the common medical image formats.\n",
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"More details, please check: https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO"
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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": 8,
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
@@ -149,23 +127,23 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(128, 128, 128)\n",
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"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",
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"image data shape:(64, 128, 96)\n",
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"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",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
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" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
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" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n"
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" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.nii.gz'}\n"
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]
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}
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],
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"source": [
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"# generate 3D test images\n",
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"tempdir = tempfile.mkdtemp()\n",
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"test_image = np.random.rand(128, 128, 128)\n",
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"test_image = np.random.rand(64, 128, 96)\n",
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"filename = os.path.join(tempdir, \"test_image.nii.gz\")\n",
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"itk_np_view = itk.image_view_from_array(test_image)\n",
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"itk.imwrite(itk_np_view, filename)\n",
@@ -179,31 +157,31 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load a list of images and stack as 1 training item\n",
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"## Load a list of Nifti images and stack as 1 training item\n",
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"Loading a list of files, stack them together and add a new dimension as first dimension.\n",
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"\n",
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"And use the meta data of the first image to represent the stacked result."
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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": 9,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(3, 128, 128, 128)\n",
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"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",
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"image data shape:(3, 64, 128, 96)\n",
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"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",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
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" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
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" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n"
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" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.nii.gz'}\n"
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]
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}
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],
@@ -219,6 +197,90 @@
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"print(f\"meta data:{meta}\")"
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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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"## Load 3D image in DICOM format"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(64, 128, 96)\n",
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"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",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
220+
" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
223+
" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
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" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.dcm'}\n"
226+
]
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}
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],
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"source": [
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"filename = os.path.join(tempdir, \"test_image.dcm\")\n",
231+
"dcm_image = np.random.randint(256, size=(64, 128, 96)).astype(np.uint8())\n",
232+
"itk_np_view = itk.image_view_from_array(dcm_image)\n",
233+
"itk.imwrite(itk_np_view, filename)\n",
234+
"data, meta = LoadImage()(filename)\n",
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"\n",
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"print(f\"image data shape:{data.shape}\")\n",
237+
"print(f\"meta data:{meta}\")"
238+
]
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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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"## Load a list of DICOM images and stack as 1 training item\n",
245+
"Loading a list of files, stack them together and add a new dimension as first dimension.\n",
246+
"\n",
247+
"And use the meta data of the first image to represent the stacked result."
248+
]
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(3, 64, 128, 96)\n",
260+
"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",
261+
" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
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" [0., 1., 0., 0.],\n",
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" [0., 0., 1., 0.],\n",
265+
" [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
266+
" [0., 1., 0., 0.],\n",
267+
" [0., 0., 1., 0.],\n",
268+
" [0., 0., 0., 1.]]), 'spatial_shape': [64, 128, 96], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.dcm'}\n"
269+
]
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}
271+
],
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"source": [
273+
"filenames = [\"test_image.dcm\", \"test_image2.dcm\", \"test_image3.dcm\"]\n",
274+
"for i, name in enumerate(filenames):\n",
275+
" filenames[i] = os.path.join(tempdir, name)\n",
276+
" itk_np_view = itk.image_view_from_array(dcm_image)\n",
277+
" itk.imwrite(itk_np_view, filenames[i])\n",
278+
"data, meta = LoadImage()(filenames)\n",
279+
"\n",
280+
"print(f\"image data shape:{data.shape}\")\n",
281+
"print(f\"meta data:{meta}\")"
282+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -228,7 +290,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 6,
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"metadata": {
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"tags": []
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},
@@ -237,18 +299,18 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(256, 256)\n",
302+
"image data shape:(128, 256)\n",
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"meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
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" [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
308+
" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
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]
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}
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],
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"source": [
251-
"test_image = np.random.randint(0, 256, size=[256, 256])\n",
313+
"test_image = np.random.randint(0, 256, size=[128, 256])\n",
252314
"filename = os.path.join(tempdir, \"test_image.png\")\n",
253315
"Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n",
254316
"data, meta = LoadImage()(filename)\n",
@@ -272,26 +334,26 @@
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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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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(256, 256)\n",
344+
"image data shape:(128, 256)\n",
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"meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
284346
" [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
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" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
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]
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}
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],
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"source": [
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"loader = LoadImage()\n",
294-
"loader.register(ITKReader(c_order_axis_indexing=True))\n",
356+
"loader.register(ITKReader())\n",
295357
"data, meta = loader(filename)\n",
296358
"\n",
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"print(f\"image data shape:{data.shape}\")\n",
@@ -310,15 +372,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image data shape:(256, 256, 2)\n",
321-
"meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 256, 'info': {}, 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
382+
"image data shape:(128, 256, 2)\n",
383+
"meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 128, 'info': {}, 'spatial_shape': [256, 128], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
322384
]
323385
}
324386
],
@@ -340,7 +402,7 @@
340402
},
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{
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"cell_type": "code",
343-
"execution_count": 23,
405+
"execution_count": 9,
344406
"metadata": {
345407
"tags": []
346408
},
@@ -355,7 +417,7 @@
355417
" [0., 1., 0.],\n",
356418
" [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
357419
" [0., 1., 0.],\n",
358-
" [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n"
420+
" [0., 0., 1.]]), 'spatial_shape': [128, 256], 'filename_or_obj': '/tmp/tmpq5gymhdr/test_image.png'}\n"
359421
]
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}
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],
@@ -383,7 +445,7 @@
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},
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{
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"cell_type": "code",
386-
"execution_count": 27,
448+
"execution_count": 10,
387449
"metadata": {},
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"outputs": [],
389451
"source": [

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