{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tce3stUlHN0L"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow IO Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:47.094381Z",
     "iopub.status.busy": "2021-11-22T03:47:47.093742Z",
     "iopub.status.idle": "2021-11-22T03:47:47.096617Z",
     "shell.execute_reply": "2021-11-22T03:47:47.096098Z"
    },
    "id": "tuOe1ymfHZPu"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qFdPvlXBOdUN"
   },
   "source": [
    "# Decode DICOM files for medical imaging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfBg1C5NB3X0"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/io/tutorials/dicom\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/io/blob/master/docs/tutorials/dicom.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/io/blob/master/docs/tutorials/dicom.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "      <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/dicom.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xHxb-dlhMIzW"
   },
   "source": [
    "## Overview\n",
    "\n",
    "This tutorial shows how to use `tfio.image.decode_dicom_image` in TensorFlow IO to decode DICOM files with TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MUXex9ctTuDB"
   },
   "source": [
    "## Setup and Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4YsfgDMZW5g6"
   },
   "source": [
    "#### Download DICOM image\n",
    "\n",
    "The DICOM image used in this tutorial is from the [NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays in PNG format, provided by NIH Clinical Center and could be downloaded through [this link](https://nihcc.app.box.com/v/ChestXray-NIHCC).\n",
    "\n",
    "Google Cloud also provides a DICOM version of the images, available in [Cloud Storage](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "In this tutorial, you will download a sample file of the dataset from the [GitHub repo](https://github.com/tensorflow/io/raw/master/docs/tutorials/dicom/dicom_00000001_000.dcm)\n",
    "\n",
    "\n",
    "Note: For more information about the dataset, please find the following reference:\n",
    "\n",
    "- Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summers, ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, IEEE CVPR, pp. 3462-3471, 2017\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:47.107948Z",
     "iopub.status.busy": "2021-11-22T03:47:47.107260Z",
     "iopub.status.idle": "2021-11-22T03:47:47.870833Z",
     "shell.execute_reply": "2021-11-22T03:47:47.870294Z"
    },
    "id": "Tu01THzWcE-J"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "100   164    0   164    0     0    600      0 --:--:-- --:--:-- --:--:--   598\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "100 1024k  100 1024k    0     0  1915k      0 --:--:-- --:--:-- --:--:-- 1915k\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 kbuilder kokoro 1049332 Nov 22 03:47 dicom_00000001_000.dcm\r\n"
     ]
    }
   ],
   "source": [
    "!curl -OL https://github.com/tensorflow/io/raw/master/docs/tutorials/dicom/dicom_00000001_000.dcm\n",
    "!ls -l dicom_00000001_000.dcm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "upgCc3gXybsA"
   },
   "source": [
    "### Install required Packages, and restart runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:47.876377Z",
     "iopub.status.busy": "2021-11-22T03:47:47.875791Z",
     "iopub.status.idle": "2021-11-22T03:47:47.877961Z",
     "shell.execute_reply": "2021-11-22T03:47:47.877430Z"
    },
    "id": "NwL3fEMQuZrk"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  # Use the Colab's preinstalled TensorFlow 2.x\n",
    "  %tensorflow_version 2.x \n",
    "except:\n",
    "  pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:47.882089Z",
     "iopub.status.busy": "2021-11-22T03:47:47.881452Z",
     "iopub.status.idle": "2021-11-22T03:47:50.307742Z",
     "shell.execute_reply": "2021-11-22T03:47:50.308174Z"
    },
    "id": "uUDYyMZRfkX4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-io\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Using cached tensorflow_io-0.22.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (22.7 MB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
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     "text": [
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      "Requirement already satisfied: oauthlib>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow<2.8.0,>=2.7.0->tensorflow-io) (3.1.1)\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tensorflow-io\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed tensorflow-io-0.22.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yZmI7l_GykcW"
   },
   "source": [
    "### Decode DICOM image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:50.313529Z",
     "iopub.status.busy": "2021-11-22T03:47:50.312978Z",
     "iopub.status.idle": "2021-11-22T03:47:52.840523Z",
     "shell.execute_reply": "2021-11-22T03:47:52.839981Z"
    },
    "id": "YUj0878jPyz7"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:52.847062Z",
     "iopub.status.busy": "2021-11-22T03:47:52.846504Z",
     "iopub.status.idle": "2021-11-22T03:47:53.884479Z",
     "shell.execute_reply": "2021-11-22T03:47:53.884865Z"
    },
    "id": "zK7IEukfuUuF"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-22 03:47:53.016507: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow_io as tfio\n",
    "\n",
    "image_bytes = tf.io.read_file('dicom_00000001_000.dcm')\n",
    "\n",
    "image = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
    "\n",
    "skipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
    "\n",
    "lossy_image = tfio.image.decode_dicom_image(image_bytes, scale='auto', on_error='lossy', dtype=tf.uint8)\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10,10))\n",
    "axes[0].imshow(np.squeeze(image.numpy()), cmap='gray')\n",
    "axes[0].set_title('image')\n",
    "axes[1].imshow(np.squeeze(lossy_image.numpy()), cmap='gray')\n",
    "axes[1].set_title('lossy image');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VbkKcNZunw3N"
   },
   "source": [
    "### Decode DICOM Metadata and working with Tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D7tuwYksn8e7"
   },
   "source": [
    "`decode_dicom_data` decodes tag information. `dicom_tags` contains useful information as the patient's age and sex, so you can use DICOM tags such as `dicom_tags.PatientsAge` and `dicom_tags.PatientsSex`. tensorflow_io borrow the same tag notation from the pydicom dicom package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:53.890648Z",
     "iopub.status.busy": "2021-11-22T03:47:53.890068Z",
     "iopub.status.idle": "2021-11-22T03:47:53.893291Z",
     "shell.execute_reply": "2021-11-22T03:47:53.893660Z"
    },
    "id": "OqHkXwF0oI3L"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'58', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "tag_id = tfio.image.dicom_tags.PatientsAge\n",
    "tag_value = tfio.image.decode_dicom_data(image_bytes,tag_id)\n",
    "print(tag_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:53.897996Z",
     "iopub.status.busy": "2021-11-22T03:47:53.897369Z",
     "iopub.status.idle": "2021-11-22T03:47:53.899993Z",
     "shell.execute_reply": "2021-11-22T03:47:53.900327Z"
    },
    "id": "J2wZ-7OcoPPs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PatientsAge : 58\n"
     ]
    }
   ],
   "source": [
    "print(f\"PatientsAge : {tag_value.numpy().decode('UTF-8')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T03:47:53.904662Z",
     "iopub.status.busy": "2021-11-22T03:47:53.904117Z",
     "iopub.status.idle": "2021-11-22T03:47:53.907039Z",
     "shell.execute_reply": "2021-11-22T03:47:53.907366Z"
    },
    "id": "Ce6ymbskoTOe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PatientsSex : M\n"
     ]
    }
   ],
   "source": [
    "tag_id = tfio.image.dicom_tags.PatientsSex\n",
    "tag_value = tfio.image.decode_dicom_data(image_bytes,tag_id)\n",
    "print(f\"PatientsSex : {tag_value.numpy().decode('UTF-8')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WodUv8O1VKmr"
   },
   "source": [
    "## Documentation\n",
    "This package has two operations which wrap `DCMTK` functions. `decode_dicom_image` decodes the pixel data from DICOM files, and `decode_dicom_data` decodes tag information. `tags` contains useful DICOM tags such as `tags.PatientsName`. The tag notation is borrowed from the [`pydicom`](https://pydicom.github.io/) dicom package.\n",
    "\n",
    "### Getting DICOM Image Data\n",
    "```python\n",
    "io.dicom.decode_dicom_image(\n",
    "    contents,\n",
    "    color_dim=False,\n",
    "    on_error='skip',\n",
    "    scale='preserve',\n",
    "    dtype=tf.uint16,\n",
    "    name=None\n",
    ")\n",
    "```\n",
    "\n",
    " - **`contents`**: A Tensor of type string. 0-D. The byte string encoded DICOM file\n",
    " - **`color_dim`**: An optional `bool`. Defaults to `False`. If `True`, a third channel will be appended to all images forming a 3-D tensor. A 1024 x 1024 grayscale image will be 1024 x 1024 x 1\n",
    " - **`on_error`**: Defaults to `skip`. This attribute establishes the behavior in case an error occurs on opening the image or if the output type cannot accomodate all the possible input values. For example if the user sets the output dtype to `tf.uint8`, but a dicom image stores a `tf.uint16` type. `strict` throws an error. `skip` returns a 1-D empty tensor.  `lossy` continues with the operation scaling the value via the `scale` attribute. \n",
    " - **`scale`**:  Defaults to `preserve`. This attribute establishes what to do with the scale of the input values. `auto` will autoscale the input values, if the output type is integer, `auto` will use the maximum output scale for example a `uint8` which stores values from [0, 255] can be linearly stretched to fill a `uint16` that is [0,65535]. If the output is float, `auto` will scale to [0,1]. `preserve` keeps the values as they are, an input value greater than the maximum possible output will be clipped. \n",
    " - **`dtype`**: An optional `tf.DType` from: `tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.float16, tf.float32, tf.float64`. Defaults to `tf.uint16`. \n",
    " - **`name`**: A name for the operation (optional).\n",
    " \n",
    " **Returns**\n",
    "   A `Tensor` of type `dtype` and the shape is determined by the DICOM file. \n",
    "\n",
    "### Getting DICOM Tag Data\n",
    "```python\n",
    "io.dicom.decode_dicom_data(\n",
    "    contents,\n",
    "    tags=None,\n",
    "    name=None\n",
    ")\n",
    "```\n",
    "\n",
    " - **`contents`**: A Tensor of type string. 0-D. The byte string encoded DICOM file\n",
    " - **`tags`**: A Tensor of type `tf.uint32` of any dimension. These `uint32` numbers map directly to DICOM tags\n",
    " - **`name`**: A name for the operation (optional).\n",
    "\n",
    " **Returns**\n",
    "   A `Tensor` of type `tf.string` and same shape as `tags`.  If a dicom tag is a list of strings, they are combined into one string and seperated by a double backslash `\\\\`. There is a bug in [DCMTK](https://support.dcmtk.org/docs/) if the tag is a list of numbers, only the zeroth element will be returned as a string.\n",
    "\n",
    "\n",
    "\n",
    "### Bibtex\n",
    "\n",
    "If this package helped, please kindly cite the below:\n",
    "\n",
    "```\n",
    "@misc{marcelo_lerendegui_2019_3337331,\n",
    "  author       = {Marcelo Lerendegui and\n",
    "                  Ouwen Huang},\n",
    "  title        = {Tensorflow Dicom Decoder},\n",
    "  month        = jul,\n",
    "  year         = 2019,\n",
    "  doi          = {10.5281/zenodo.3337331},\n",
    "  url          = {https://doi.org/10.5281/zenodo.3337331}\n",
    "}\n",
    "```\n",
    "\n",
    "### License\n",
    "\n",
    "Copyright 2019 Marcelo Lerendegui, Ouwen Huang, Gradient Health Inc.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "   http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  }
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