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+{
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+ "metadata": {
+ "id": "7d6a1a99-7979-41e5-9a3d-4c94918da61b"
+ },
+ "source": [
+ "# DICOM SEG\n",
+ "\n",
+ "This example demonstrates how to read DICOM CT volumes with and DICOM SEG, AI-generated segmentations, with [ITK-Wasm](https://wasm.itk.org). Data is pulled from the [NIH Imaging Data Commons (IDC)](https://portal.imaging.datacommons.cancer.go).\n",
+ "\n",
+ "You can run this notebook locally or with any of the following platforms: \n",
+ "[](https://colab.research.google.com/github/InsightSoftwareConsortium/itkwidgets/blob/main/examples/integrations/itkwasm/DICOM_SEG.ipynb)\n",
+ "[](https://mybinder.org/v2/gh/InsightSoftwareConsortium/itkwidgets/HEAD?labpath=examples%2Fintegrations%2Fitkwasm%2FDICOM_SEG.ipynb)\n",
+ "[](https://studiolab.sagemaker.aws/import/github.com/InsightSoftwareConsortium/itkwidgets/blob/main/examples/integrations/itkwasm/DICOM_SEG.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c98ddf17-b608-448c-9253-cd7b806b96db",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c98ddf17-b608-448c-9253-cd7b806b96db",
+ "outputId": "f0c84ed1-a871-42e5-bc6f-eec881e469a9",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "!{sys.executable} -m pip install -q \"itkwidgets[all]>=1.0a49\" rich s5cmd itkwasm-dicom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4120f9bf-e7f3-48a1-ab14-ce57b4d95621",
+ "metadata": {
+ "id": "4120f9bf-e7f3-48a1-ab14-ce57b4d95621",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "import glob\n",
+ "from itkwidgets import view\n",
+ "from itkwasm_dicom import read_segmentation, read_image_dicom_file_series\n",
+ "import numpy as np\n",
+ "from rich import print"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb85dba6-d7e3-4996-9f1d-7598b184ae50",
+ "metadata": {
+ "id": "fb85dba6-d7e3-4996-9f1d-7598b184ae50"
+ },
+ "source": [
+ "## Segmentation of lung cancer from CT series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "aOnyE_7hllIl",
+ "metadata": {
+ "id": "aOnyE_7hllIl",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "# CT series downloaded from TCIA / IDC, NSCLC Radiogenomics collection, https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/#citations\n",
+ "# Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Zhang, W., Leung, A., Kadoch, M., Shrager, J., Quon, A., Rubin, D., Plevritis, S., & Napel, S. (2017). Data for NSCLC Radiogenomics (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.7hs46erv\n",
+ "!s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com cp \"s3://idc-open-data/d3d3f9a5-c90a-4763-9a4b-089aab391438/*\" CT_DICOM_series\n",
+ "\n",
+ "# Segmentation of this series downloaded from TCIA, IDC, BAMF under the AIMI Annotations initiative (https://zenodo.org/doi/10.5281/zenodo.8345959)\n",
+ "!s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com cp \"s3://idc-open-data/410e0c21-29e1-45e2-9c6b-15ae592e571b/*\" SEG_DICOM_series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a6472390-89a1-4235-8d79-f5d05b749867",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ct_image_files = glob.glob('CT_DICOM_series/*')\n",
+ "ct_image, sorted_file_names = read_image_dicom_file_series(ct_image_files)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "0e588f46-01b4-4ae4-886b-63742d59984d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Row direction: 1 0 0\n",
+ "Col direction: 0 1 0\n",
+ "Z direction: 0 0 1\n",
+ "Total frames: 126\n",
+ "Total frames with unique IPP: 76\n",
+ "Total overlapping frames: 50\n",
+ "Origin: [-348.177, -348.177, -442.83]\n",
+ "Slice extent: 245.25\n",
+ "Slice spacing: 3.26999\n",
+ "Image Orientation Patient set to : 1, 0, 0, 0, 1, 0\n",
+ "Identified 1 groups of non-overlapping segments\n"
+ ]
+ }
+ ],
+ "source": [
+ "seg_file_name = glob.glob('./SEG_DICOM_series/*.dcm')[0]\n",
+ "seg_image, seg_info = read_segmentation(seg_file_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "264e1ce0-dd11-4197-a033-574d601cab50",
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+ "outputId": "0dd9122e-599b-47ac-83ec-e2af41f1657e",
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