 Prepare 3D CT LungSegmentation Data for U-Net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from _3D_LungSegmentation_Threshold import get_segmented_lungs\n",
    "from _3D_CT_Preprocess import preprocessScan\n",
    "from _3D_CT_Preprocess import getScan\n",
    "from _3D_CT_Preprocess import normalizeScan\n",
    "from _3D_CT_Preprocess import resizeScan\n",
    "import numpy as np\n",
    "import SimpleITK as sitk\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import ndimage\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. Get CT data and lung mask data from LUNA16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_Scan_Path = \"/Users/phoenixji/Desktop/LungData/LUNA16/AllSet/\"\n",
    "Lung_Path = '/Users/phoenixji/Desktop/LungData/seg-lungs-LUNA16/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scan_List = os.listdir(CT_Scan_Path)\n",
    "Mask_List = os.listdir(Lung_Path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1776, 1776)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Scan_List),len(Mask_List)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_Scan_List=[]\n",
    "Lung_Mask_List=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "for scan in Scan_List:\n",
    "    \n",
    "    if scan.endswith('.mhd'):\n",
    "        CT_Scan_List.append(scan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "for mask in Mask_List:\n",
    "    \n",
    "    if mask.endswith('.mhd'):\n",
    "        Lung_Mask_List.append(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(888, 888)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CT_Scan_List),len(Lung_Mask_List)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_Scan=[]\n",
    "Lung_Mask=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "for Scan in CT_Scan_List:\n",
    "    \n",
    "    # Add the loriginal CT\n",
    "    scan = preprocessScan(CT_Scan_Path+Scan,(128,128,64))\n",
    "    CT_Scan.append(scan)\n",
    "    \n",
    "    # Add the lung mask\n",
    "    scan = getScan(Lung_Path+Scan)\n",
    "    \n",
    "    if(scan.shape[0] != scan.shape[1]):\n",
    "        scan = np.swapaxes(scan,0,1)  #(64,128,128) => (128,128,64)\n",
    "        scan = np.swapaxes(scan,1,2)\n",
    "    \n",
    "    scan = resizeScan(scan,(128,128,64))\n",
    "    scan = np.where(scan==0,scan,1)  # Update to only 2 labels\n",
    "    \n",
    "    Lung_Mask.append(scan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(888, 888)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CT_Scan),len(Lung_Mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((128, 128, 64), (128, 128, 64))"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CT_Scan[0].shape, Lung_Mask[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int16)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(Lung_Mask[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd78c6f00a0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(CT_Scan[3][:,:,30],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd9f7fe9dc0>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Lung_Mask[3][:,:,50],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cover data to the np array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((710, 128, 128, 64),\n",
       " (178, 128, 128, 64),\n",
       " (710, 128, 128, 64),\n",
       " (178, 128, 128, 64))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(np.array(CT_Scan), np.array(Lung_Mask), \n",
    "                                                    test_size = 0.20, random_state = 0,shuffle=True)\n",
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('X_train_3D_lungSeg',X_train)\n",
    "np.save('X_test_3D_lungSeg',X_test)\n",
    "np.save('y_train_3D_lungSeg',y_train)\n",
    "np.save('y_test_3D_lungSeg',y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins, _ = plt.hist(scanTest[20]) # hist contains the list of pixels with the same intensity for each intensity in the picture\n",
    "plt.show() #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NonCovidScan_Path=[]\n",
    "CovidScan_Path=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "NonCovidScan_PathName = \"/Users/phoenixji/Desktop/LungData/MosMedData/CT-0/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "NonCovidScan_Path = os.listdir(NonCovidScan_PathName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(NonCovidScan_Path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = []\n",
    "label = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for scan in NonCovidScan_Path:\n",
    "    \n",
    "    scan = getScan(NonCovidScan_PathName+scan)\n",
    "    mask = np.array([get_segmented_lungs(slice.copy()) for slice in scan])\n",
    "    scan[~mask] = 0\n",
    "    scan = normalizeScan(scan)\n",
    "    print(\"Before Resize\"+str(scan.shape))\n",
    "    scan = np.swapaxes(scan,0,1)  #(64,128,128) => (128,128,64)\n",
    "    scan = np.swapaxes(scan,1,2)\n",
    "    scan = resizeScan_(scan,(128,128,64))\n",
    "    scan = np.expand_dims(scan,axis=-1)\n",
    "    print(\"After Resize\"+str(scan.shape))\n",
    "    dataset.append(scan)\n",
    "    label.append(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "854"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "854"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-b93d914d547a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m680\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.imshow(dataset[680][:,:,35],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "CovidScan_PathName_1 = \"/Users/phoenixji/Desktop/LungData/MosMedData/CT-1/\"\n",
    "CovidScan_PathName_2 = \"/Users/phoenixji/Desktop/LungData/MosMedData/CT-2/\"\n",
    "CovidScan_PathName_3 = \"/Users/phoenixji/Desktop/LungData/MosMedData/CT-3/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "CovidScan_Path = os.listdir(CovidScan_PathName_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CovidScan_Path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#改三次\n",
    "for scan in CovidScan_Path:\n",
    "    \n",
    "    scan = getScan(CovidScan_PathName_3+scan)\n",
    "    mask = np.array([get_segmented_lungs(slice.copy()) for slice in scan])\n",
    "    scan[~mask] = 0\n",
    "    scan = normalizeScan(scan)\n",
    "    print(\"Before Resize\"+str(scan.shape))\n",
    "    scan = np.swapaxes(scan,0,1)  #(64,128,128) => (128,128,64)\n",
    "    scan = np.swapaxes(scan,1,2)\n",
    "    scan = resizeScan_(scan,(128,128,64))\n",
    "    scan = np.expand_dims(scan,axis=-1)\n",
    "    print(\"After Resize\"+str(scan.shape))\n",
    "    dataset.append(scan)\n",
    "    label.append(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "710"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((683, 128, 128, 64, 1), (171, 128, 128, 64, 1), (683,), (171,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(np.array(dataset), np.array(label), \n",
    "                                                    test_size = 0.20, random_state = 0,shuffle=True)\n",
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('X_train_lungSeg_pos3class',X_train)\n",
    "np.save('X_test_lungSeg_pos3class',X_test)\n",
    "np.save('y_train_lungSeg_pos3class',y_train)\n",
    "np.save('y_test_lungSeg_pos3class',y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('dataset',dataset)\n",
    "np.save('label',label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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