04_FPR_Candidate_ROI_extraction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8ZTHKw-uMNT",
        "outputId": "3daf0acc-b858-4973-cfa5-d470608c6fc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting SimpleITK\n",
            "  Downloading SimpleITK-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: SimpleITK\n",
            "Successfully installed SimpleITK-2.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install SimpleITK"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rc1WMf6kuO6F",
        "outputId": "6d50be0d-eb0e-468b-a66a-5293d4eb6ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNnn2HzbuZKO",
        "outputId": "728cf20c-0eb0-4965-c748-19a6035c1d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "yBQ6k_QXubbu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/gdrive/MyDrive/kaggle.json ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "-Nm4N1sVugsL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download -d wuwu55/luna16"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nmi1njjguwAZ",
        "outputId": "0075e8b9-4c3d-40c2-b37d-e97a91169826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading luna16.zip to /content\n",
            "100% 17.6G/17.6G [03:01<00:00, 142MB/s]\n",
            "100% 17.6G/17.6G [03:01<00:00, 104MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip luna16.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5sPQLETuyaW",
        "outputId": "1382ed15-3921-46f1-8abf-60aa289cfcfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  luna16.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage import measure\n",
        "import SimpleITK as stk\n",
        "from glob import glob\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "PCnthXWHvg-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "root = \"/content/subset6/\"\n",
        "target_root = \"/content/gdrive/MyDrive/FPRProcessed\""
      ],
      "metadata": {
        "id": "uGGMOExwxj0U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset = 6\n",
        "file_list = glob(root+f\"subset{subset}/*.mhd\")\n",
        "print(\"Files Count:\",len(file_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uyH9pvWyLOB",
        "outputId": "1b4df51f-082f-46bd-c862-956ea8d59fa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files Count: 89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidates_df = pd.read_csv(\"/content/gdrive/MyDrive/candidates.csv\")"
      ],
      "metadata": {
        "id": "v9r7z-n9yNlq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "candidates_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PCrrjzo9ySOO",
        "outputId": "c8064d72-a8b0-436f-cf67-11e2a1f464b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-e5dde92d-514f-494f-94d5-8b169bc445cb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e5dde92d-514f-494f-94d5-8b169bc445cb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Total Candidates:\",len(candidates_df))\n",
        "print(\"Positives:\",candidates_df['class'].sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEolPMh0yUMG",
        "outputId": "e28c4b42-d4b3-477f-bd25-838d410e0c5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Candidates: 551065\n",
            "Positives: 1351\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidates_df.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12AGaCMKyWWO",
        "outputId": "46c77445-712e-481d-e419-2e8dcd11d1f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 551065 entries, 0 to 551064\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   seriesuid  551065 non-null  object \n",
            " 1   coordX     551065 non-null  float64\n",
            " 2   coordY     551065 non-null  float64\n",
            " 3   coordZ     551065 non-null  float64\n",
            " 4   class      551065 non-null  int64  \n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 21.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_filename(file_list, file):\n",
        "    for f in file_list:\n",
        "        if file in f:\n",
        "            return f"
      ],
      "metadata": {
        "id": "Rq5ZqwmOyYbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_mhd(file):\n",
        "    mhdimage = stk.ReadImage(file)\n",
        "    ct_scan = stk.GetArrayFromImage(mhdimage)\n",
        "    origin = np.array(list(mhdimage.GetOrigin()))\n",
        "    space = np.array(list(mhdimage.GetSpacing()))\n",
        "    return ct_scan, origin, space\n"
      ],
      "metadata": {
        "id": "tfUTBaQQyazU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "candidates_df[\"filename\"] = candidates_df[\"seriesuid\"].map(lambda file: get_filename(file_list, file))\n",
        "candidates_df = candidates_df.dropna()\n",
        "print(len(candidates_df))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGxBsuLeycn1",
        "outputId": "e5297915-1a6f-4c1b-e04f-15ad9beaf638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52537\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidates_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hivB1vnPyegE",
        "outputId": "d2c74871-b3b0-4b9f-f537-f4d90b979a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       coordZ  class                                           filename  \n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f62dcd7f-f496-41a4-a8ab-1d73f3b91dba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))  # CLAHE(Contrast Limited Adaptive Histogram Equalization) filter for enhancing the contrast of an image\n",
        "for i,file in tqdm(enumerate(np.unique(candidates_df['filename'].values)), total=len(np.unique(candidates_df['filename'].values))):\n",
        "    candidates = candidates_df[candidates_df[\"filename\"]==file]\n",
        "    ct, origin, space = load_mhd(file)\n",
        "    num_z, height, width = ct.shape\n",
        "    ct_norm = cv2.normalize(ct, None, 0, 255, cv2.NORM_MINMAX)\n",
        "    for idx, row in candidates.iterrows():\n",
        "        node_x = int(row[\"coordX\"])     # x-coordinate of the candidate\n",
        "        node_y = int(row[\"coordY\"])     # y-coordinate of the candidate\n",
        "        node_z = int(row[\"coordZ\"])     # z-coordinate of the candidate\n",
        "        c = int(row[\"class\"])           # class of the candidate (1: nodule, 0: non-nodule)\n",
        "\n",
        "        center = np.array([node_x, node_y, node_z])   # nodule center\n",
        "        v_center = np.rint((center-origin)/space)   # nodule center in voxel space (still x,y,z ordering)\n",
        "\n",
        "        img_norm = ct_norm[int(v_center[2]),:,:]    # a slice of the CT scan containing the candidate\n",
        "        img_norm = cv2.resize(img_norm, (512,512))  # resize the image to 512x512\n",
        "        img_norm_improved = clahe.apply(img_norm.astype(np.uint8))  # apply CLAHE filter to the image\n",
        "\n",
        "        x=abs(int(v_center[0]))\n",
        "        y=abs(int(v_center[1]))\n",
        "        box = img_norm_improved[max(0,y-25):min(y+25,512),max(0,x-25):min(x+25,512)]    # extract a box of size 25x25 around the candidate\n",
        "        if box.shape != (50,50):\n",
        "            box = cv2.resize(box, (50,50))\n",
        "\n",
        "        if c:  # if the candidate is a nodule\n",
        "            # applying different image transformations to increase the number of nodule candidates\n",
        "            cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}.jpg\"),box)\n",
        "            cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_1.jpg\"),cv2.rotate(box,cv2.ROTATE_90_CLOCKWISE))\n",
        "            cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_2.jpg\"),cv2.rotate(box, cv2.ROTATE_90_COUNTERCLOCKWISE))\n",
        "            cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_3.jpg\"),cv2.rotate(box, cv2.ROTATE_180))\n",
        "            cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_4.jpg\"),cv2.flip(box, 1))\n",
        "        else:  # if the candidate is not a nodule\n",
        "            cv2.imwrite(os.path.join(target_root+\"/non-nodule-initial/\", f\"candidate_{subset}_{c}_{idx}.jpg\"),box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cyDeQ6kyhLV",
        "outputId": "39641cdf-e8d8-4761-dde0-489c2838d51f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 89/89 [09:22<00:00,  6.32s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kTLaNc_wynkQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}