GettingDicomData.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GettingDicomData.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCfbz0PZ2H5o",
        "colab_type": "text"
      },
      "source": [
        "This cell will attempt to download all of the data from the google bucket. Not recommended as the amount of data will require alot of space and time. If running from google colab the storage will be innadequate. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G18VB1i1Y0E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gsutil -q -m cp -p -r \"gs://gbm_tcga\" /tmp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR4Tpcp43MVy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkDG8yh5F3ok",
        "colab_type": "text"
      },
      "source": [
        "RUN THIS AND THE NEXT CELL FIRST TO BEGIN THE CREDENTIALING PROCESS. THE GOOGLE DRIVE WILL NOT BE MOUNTABLE OTHERWISE. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ixhDWEvoekw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.cloud import storage\n",
        "from google.colab import auth"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4o3Wnu25oVRh",
        "colab_type": "code",
        "outputId": "84b077de-cc0e-4ac2-efe1-4c569429b832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "auth.authenticate_user()\n",
        "client = storage.Client(project='peak-trilogy-248117')\n",
        "\n",
        "buckets = client.list_buckets()\n",
        "\n",
        "print(\"Buckets in {}:\".format(client.project))\n",
        "for item in buckets:\n",
        "    print(\"\\t\" + item.name)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Buckets in peak-trilogy-248117:\n",
            "\tgbm_tcga\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZzfffqae1sa",
        "colab_type": "code",
        "outputId": "1ded6907-25fe-4195-8122-651879de3215",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "bucket = client.get_bucket('gbm_tcga')\n",
        "\n",
        "print(\"Bucket name: {}\".format(bucket.name))\n",
        "print(\"Bucket location: {}\".format(bucket.location))\n",
        "print(\"Bucket storage class: {}\".format(bucket.storage_class))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Bucket name: gbm_tcga\n",
            "Bucket location: US\n",
            "Bucket storage class: MULTI_REGIONAL\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12ZreNSYBiiC",
        "colab_type": "text"
      },
      "source": [
        "This cell will list all of the files that are \"T1.\" If you run may take a while and the amount of output will likely crash the notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53KSuS5rfEQI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "blobs = bucket.list_blobs()\n",
        "\n",
        "print(\"Blobs in {}:\".format(bucket.name))\n",
        "for item in blobs:\n",
        "  if \"T1\" in item.name:\n",
        "    print(\"\\t\" + item.name)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4PN3JwlFV9O",
        "colab_type": "text"
      },
      "source": [
        "THIS MUST BE RUN AFTER RUNNING THE CELL MARKED WITH RUN THIS FIRST!!!!!!!! The google storage will not be accessed otherwise due to credential issues. I'm not sure why they have to be run in this order or why you have to run both but that is the only way I could get it to work. If it asks you if you want to continue say yes or y. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4F6Qxb6NLBPL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "6ce8eb20-3c72-428a-8a7a-a781d47dceab"
      },
      "source": [
        "!gcloud auth application-default login"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The environment variable [GOOGLE_APPLICATION_CREDENTIALS] is set to:\n",
            "  [/content/adc.json]\n",
            "Credentials will still be generated to the default location:\n",
            "  [/content/.config/application_default_credentials.json]\n",
            "To use these credentials, unset this environment variable before\n",
            "running your application.\n",
            "\n",
            "Do you want to continue (Y/n)?  y\n",
            "\n",
            "Go to the following link in your browser:\n",
            "\n",
            "    https://accounts.google.com/o/oauth2/auth?redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&prompt=select_account&response_type=code&client_id=764086051850-6qr4p6gpi6hn506pt8ejuq83di341hur.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform&access_type=offline\n",
            "\n",
            "\n",
            "Enter verification code: 4/lgFJjPNYFgx0Ghpa67KRZhwsd-eppvdeepM9rjN93a61gxLYb4T33kA\n",
            "\n",
            "Credentials saved to file: [/content/.config/application_default_credentials.json]\n",
            "\n",
            "These credentials will be used by any library that requests\n",
            "Application Default Credentials.\n",
            "\n",
            "To generate an access token for other uses, run:\n",
            "  gcloud auth application-default print-access-token\n",
            "\n",
            "\n",
            "To take a quick anonymous survey, run:\n",
            "  $ gcloud alpha survey\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2j_I4xw5CBVE",
        "colab_type": "text"
      },
      "source": [
        "This cell will download the required packages and dependencies so as to allow you to run gcsfuse. Gcsfuse basically mounts the google cloud storage which allows your system to treat the the storage file as if it were another file on the system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaS14KirAQ9K",
        "colab_type": "code",
        "outputId": "04d87e93-b434-4d88-a2ee-e1d0c702cf00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "source": [
        "!echo \"deb http://packages.cloud.google.com/apt gcsfuse-bionic main\" > /etc/apt/sources.list.d/gcsfuse.list\n",
        "!curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -\n",
        "!apt -qq update\n",
        "!apt -qq install gcsfuse"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   659  100   659    0     0  25346      0 --:--:-- --:--:-- --:--:-- 26360\n",
            "OK\n",
            "26 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  gcsfuse\n",
            "0 upgraded, 1 newly installed, 0 to remove and 26 not upgraded.\n",
            "Need to get 4,274 kB of archives.\n",
            "After this operation, 12.8 MB of additional disk space will be used.\n",
            "Selecting previously unselected package gcsfuse.\n",
            "(Reading database ... 131289 files and directories currently installed.)\n",
            "Preparing to unpack .../gcsfuse_0.28.1_amd64.deb ...\n",
            "Unpacking gcsfuse (0.28.1) ...\n",
            "Setting up gcsfuse (0.28.1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v66FLb5RSqgf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.cloud import storage\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3Et0UKQ_irY",
        "colab_type": "code",
        "outputId": "227c4ab6-fecd-4256-e4e6-4a7eacd78c68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "!mkdir /tmp/tcga-gbm\n",
        "!gcsfuse --implicit-dirs gbm_tcga /tmp/tcga-gbm"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using mount point: /tmp/tcga-gbm\n",
            "Opening GCS connection...\n",
            "Opening bucket...\n",
            "Mounting file system...\n",
            "File system has been successfully mounted.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPup_qp5Cav_",
        "colab_type": "text"
      },
      "source": [
        "Run this basically as a sanity check. Should list all the files in the bucket. If you get an input output error message, it is likely that your credentials weren't properly established which is probably a result of you having ran the above cells in the wrong order or missed a cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjPVSXG8Ay_b",
        "colab_type": "code",
        "outputId": "ffc95a32-a132-4779-e017-e6fef2bab9a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls /tmp/tcga-gbm"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "file_names.csv\tTCGA-GBM\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zk2Nkn1PDLMh",
        "colab_type": "text"
      },
      "source": [
        "This cell will search the entire directory for all filepaths containing \"T1.\" Warning, this took several hours when I ran it. Files seem to load quickly but mapping the drive takes a very long time this way."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jalRLxcEiQ1",
        "colab_type": "code",
        "outputId": "0208b078-22b2-4aec-9430-59e294d1447c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os\n",
        "from tqdm import tqdm\n",
        "\n",
        "T1_files = []\n",
        "\n",
        "for root, dirs, files in tqdm(os.walk(\"/tmp/tcga-gbm\")):\n",
        "    for dir in dirs:\n",
        "      if \"T1\" in dir:\n",
        "        T1_files.append(os.path.join(root, dir))\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6251it [3:23:53,  2.00s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSXivKGtDc9y",
        "colab_type": "text"
      },
      "source": [
        "I am using the pydicom package for handling the dicom/ .dcm files. Load it into the notebook using this command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91Vz8Xv4oSPG",
        "colab_type": "code",
        "outputId": "83ecd9ce-b5fe-4b04-ee79-3f923690db30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "!pip install -U pydicom"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydicom\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/d5/da1fdf3b967e324ee47a7ad9553c9b94c1193b6b98afd9eeda0efb76b9f7/pydicom-1.3.0-py2.py3-none-any.whl (7.1MB)\n",
            "\u001b[K     |████████████████████████████████| 7.1MB 4.9MB/s \n",
            "\u001b[?25hInstalling collected packages: pydicom\n",
            "Successfully installed pydicom-1.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCGzDnWDDmrb",
        "colab_type": "text"
      },
      "source": [
        "This cell creates a interface to allow you to scroll through the stacked dicom images. Scrolling does not work in a notebook. but there should be a sample image from the stack print below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6xT8TtwglS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a43de964-c372-4fc0-eaa5-743eb3808a89"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os, glob\n",
        "import pydicom\n",
        "import pylab as pl\n",
        "import sys\n",
        "import matplotlib.path as mplPath\n",
        "\n",
        "class IndexTracker(object):\n",
        "    def __init__(self, ax, X):\n",
        "        self.ax = ax\n",
        "        ax.set_title('Scroll to Navigate through the DICOM Image Slices')\n",
        "\n",
        "        self.X = X\n",
        "        rows, cols, self.slices = X.shape\n",
        "        self.ind = self.slices//2\n",
        "\n",
        "        self.im = ax.imshow(self.X[:, :, self.ind])\n",
        "        self.update()\n",
        "\n",
        "    def onscroll(self, event):\n",
        "        print(\"%s %s\" % (event.button, event.step))\n",
        "        if event.button == 'up':\n",
        "            self.ind = (self.ind + 1) % self.slices\n",
        "        else:\n",
        "            self.ind = (self.ind - 1) % self.slices\n",
        "        self.update()\n",
        "\n",
        "    def update(self):\n",
        "        self.im.set_data(self.X[:, :, self.ind])\n",
        "        ax.set_ylabel('Slice Number: %s' % self.ind)\n",
        "        self.im.axes.figure.canvas.draw()\n",
        "\n",
        "fig, ax = plt.subplots(1,1)\n",
        "\n",
        "# load the csv with the correct file paths\n",
        "files = np.loadtxt('/tmp/tcga-gbm/file_names.csv', dtype=str, delimiter=',')\n",
        "\n",
        "# set the main directory for the glob command to search over\n",
        "os.chdir(files[0])\n",
        "\n",
        "plots = []\n",
        "frames = []\n",
        "\n",
        "# search over the file from the defined file path for all dicom images \n",
        "for file in glob.glob(\"*.dcm\"):\n",
        "    ds = pydicom.dcmread(file)\n",
        "    pix = ds.pixel_array\n",
        "    frames.append(pix)\n",
        "    pix = pix*1+(-1024)\n",
        "    plots.append(pix)\n",
        "\n",
        "y = np.dstack(plots)\n",
        "\n",
        "tracker = IndexTracker(ax, y)\n",
        "\n",
        "fig.canvas.mpl_connect('scroll_event', tracker.onscroll)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fLcNfvcPYCuiI+cyw3BSWm8LJO66wBtTPv9jQZKL/7hT/rz8ZJhNdP4jSDbqt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sq+/J52nO51WybIzXytwqV114GSMFG31Fpy6gkWg+koPnaMNGcEhDov90MYGpTjBOJB3kxXI3Zv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\nAbimNa3p3NJaAK5pTWs6t/T/Af1rTBKrmpjXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWCWOe6ID8z3",
        "colab_type": "text"
      },
      "source": [
        "This is the shape that the sample dicom image stack has."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm46HqNDjKAq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5dec4829-48b2-47bf-8813-90e6d57c14a4"
      },
      "source": [
        "frames = np.array(frames)\n",
        "frames.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 256, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    }
  ]
}