[c6d664]: / demo_ct_lung_segment.ipynb

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "import os \n",
    "from scipy.spatial import distance\n",
    "from scipy import ndimage\n",
    "from skimage import measure\n",
    "from scipy import stats\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Pre-defined_ parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {}\n",
    "\n",
    "#####################################################\n",
    "# Parameters for intensity (fixed)\n",
    "#####################################################\n",
    "\n",
    "params['lungMinValue']      = -1024\n",
    "params['lungMaxValue']      = -400\n",
    "params['lungThreshold']     = -900\n",
    "\n",
    "#####################################################\n",
    "# Parameters for lung segmentation (fixed)\n",
    "#####################################################\n",
    "\n",
    "params['xRangeRatio1']      = 0.4\n",
    "params['xRangeRatio2']      = 0.75\n",
    "params['zRangeRatio1']      = 0.5\n",
    "params['zRangeRatio2']      = 0.75\n",
    "\n",
    "#####################################################\n",
    "# Parameters for airway segmentation\n",
    "# NEED TO ADAPT for image resolution and orientation \n",
    "#####################################################\n",
    "params['airwayRadiusMask']  = 15  # increase the value if you have high resolution image\n",
    "params['airwayRadiusX']     = 8   # ditto\n",
    "params['airwayRadiusZ']     = 15  # ditto\n",
    "params['super2infer']       = 0   # value = 1 if slice no. increases from superior to inferior, else value = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Pre-defined_ functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#####################################################\n",
    "# Generate binary structure to mimic trachea\n",
    "#####################################################\n",
    "\n",
    "def generate_structure_trachea(Radius, RadiusX, RadiusZ):\n",
    "    \n",
    "    struct_trachea = np.zeros([2*Radius+1,2*Radius+1,RadiusZ])\n",
    "    for i in range(0,2*Radius+1):\n",
    "        for j in range(0,2*Radius+1):\n",
    "            if distance.euclidean([Radius+1,Radius+1],[i,j]) < RadiusX:\n",
    "                struct_trachea[i,j,:] = 1\n",
    "            else:\n",
    "                struct_trachea[i,j,:] = 0\n",
    "    \n",
    "    return struct_trachea\n",
    "\n",
    "#####################################################\n",
    "# Generate bounding box\n",
    "#####################################################\n",
    "\n",
    "def bbox2_3D(img,label,margin,limit):\n",
    "    \n",
    "    imgtmp = np.zeros(img.shape)\n",
    "    imgtmp[img == label] = 1\n",
    "    \n",
    "    x = np.any(imgtmp, axis=(1, 2))\n",
    "    y = np.any(imgtmp, axis=(0, 2))\n",
    "    z = np.any(imgtmp, axis=(0, 1))\n",
    "\n",
    "    xmin, xmax = np.where(x)[0][[0, -1]]\n",
    "    ymin, ymax = np.where(y)[0][[0, -1]]\n",
    "    zmin, zmax = np.where(z)[0][[0, -1]]\n",
    "\n",
    "    xmin = xmin - margin - 1\n",
    "    xmin = max(0,xmin)\n",
    "    ymin = ymin - margin - 1\n",
    "    ymin = max(0,ymin)\n",
    "    zmin = zmin - margin - 1\n",
    "    zmin = max(0,zmin)        \n",
    "    xmax = xmax + margin + 1\n",
    "    xmax = min(xmax,limit[0])\n",
    "    ymax = ymax + margin + 1\n",
    "    ymax = min(ymax,limit[1])\n",
    "    zmax = zmax + margin + 1\n",
    "    zmax = min(zmax,limit[2])\n",
    "        \n",
    "    return xmin, xmax, ymin, ymax, zmin, zmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Coarse_ segmentation of lung & airway \n",
    "1. Intensity thresholding and morphological operations;\n",
    "2. Select the largest connected components in estimated lung filed of view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 271.5, 239.5, -0.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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0ks+Nz1Od0lx5gkplpqEa56tcLpvmaitXrvz9/yjrVrdL2GoG6HNFV/xyBwIBrF27FqtX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc6c290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HVChF2ooJaW48TafHbop0QByLy+VCPB7HyMiIPEPOkebN+XOtbWeFa6VSgWEY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\nzp85ggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10494fb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####################################################\n",
    "# Load image \n",
    "#####################################################\n",
    "I         = nib.load('./data/sample_ct.nii.gz')\n",
    "I_affine  = I.affine\n",
    "I         = I.get_data()\n",
    "\n",
    "#####################################################\n",
    "# Intensity thresholding & Morphological operations\n",
    "#####################################################\n",
    "\n",
    "M = np.zeros(I.shape)\n",
    "M[I > params['lungMinValue']] = 1\n",
    "M[I > params['lungMaxValue']] = 0\n",
    "\n",
    "struct_s = ndimage.generate_binary_structure(3, 1)\n",
    "struct_m = ndimage.iterate_structure(struct_s, 2)\n",
    "struct_l = ndimage.iterate_structure(struct_s, 3)\n",
    "M = ndimage.binary_closing(M, structure=struct_s, iterations = 1)\n",
    "M = ndimage.binary_opening(M, structure=struct_m, iterations = 1)\n",
    "\n",
    "#####################################################\n",
    "# Estimate lung filed of view\n",
    "#####################################################\n",
    "\n",
    "[m, n, p] = I.shape;\n",
    "medx      = int(m/2)\n",
    "medy      = int(n/2)\n",
    "xrange1   = int(m/2*params['xRangeRatio1'])\n",
    "xrange2   = int(m/2*params['xRangeRatio2'])\n",
    "zrange1   = int(p*params['zRangeRatio1'])\n",
    "zrange2   = int(p*params['zRangeRatio2'])\n",
    "\n",
    "#####################################################\n",
    "# Select largest connected components & save nii\n",
    "#####################################################\n",
    "\n",
    "M = measure.label(M)\n",
    "label1 = M[medx - xrange2 : medx - xrange1, medy, zrange1 : zrange2]\n",
    "label2 = M[medx + xrange1 : medx + xrange2, medy, zrange1 : zrange2]\n",
    "label1 = stats.mode(label1[label1 > 0])[0][0]\n",
    "label2 = stats.mode(label2[label2 > 0])[0][0]\n",
    "M[M == label1] = -1\n",
    "M[M == label2] = -1\n",
    "M[M > 0] = 0\n",
    "M = M*-1\n",
    "\n",
    "M     = ndimage.binary_closing(M, structure = struct_m, iterations = 1)\n",
    "M     = ndimage.binary_fill_holes(M)\n",
    "Mlung = np.int8(M)\n",
    "nib.Nifti1Image(Mlung,I_affine).to_filename('./result/sample_lungaw.nii.gz')\n",
    "\n",
    "#####################################################\n",
    "# Display segmentation results \n",
    "#####################################################\n",
    "\n",
    "plt.figure(1)\n",
    "slice_no = int(p/2)\n",
    "plt.subplot(121)\n",
    "plt.imshow(np.fliplr(np.rot90(I[:,:,slice_no])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.fliplr(np.rot90(Mlung[:,:,slice_no])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "\n",
    "plt.figure(2)\n",
    "slice_no = int(n*0.5)\n",
    "plt.subplot(121)\n",
    "plt.imshow(np.fliplr(np.rot90(I[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.fliplr(np.rot90(Mlung[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Romove airway from lung mask\n",
    "1. Locate an inital point of the airway;\n",
    "2. Segment airway with closed space diallation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial location = [122, 133, 237]\n",
      "iter = 1 airway sum = 3077 airway change = 1239\n",
      "iter = 2 airway sum = 4309 airway change = 1232\n",
      "iter = 3 airway sum = 5548 airway change = 1239\n",
      "iter = 4 airway sum = 6616 airway change = 1068\n",
      "iter = 5 airway sum = 7459 airway change = 843\n",
      "iter = 6 airway sum = 8296 airway change = 837\n",
      "iter = 7 airway sum = 9150 airway change = 854\n",
      "iter = 8 airway sum = 9993 airway change = 843\n",
      "iter = 9 airway sum = 10920 airway change = 927\n",
      "iter = 10 airway sum = 11913 airway change = 993\n",
      "iter = 11 airway sum = 12998 airway change = 1085\n",
      "iter = 12 airway sum = 14176 airway change = 1178\n",
      "iter = 13 airway sum = 15172 airway change = 996\n",
      "iter = 14 airway sum = 16150 airway change = 978\n",
      "iter = 15 airway sum = 17117 airway change = 967\n",
      "iter = 16 airway sum = 17955 airway change = 838\n",
      "iter = 17 airway sum = 18842 airway change = 887\n",
      "iter = 18 airway sum = 19773 airway change = 931\n",
      "iter = 19 airway sum = 20557 airway change = 784\n",
      "iter = 20 airway sum = 21078 airway change = 521\n",
      "iter = 21 airway sum = 21470 airway change = 392\n",
      "iter = 22 airway sum = 21801 airway change = 331\n",
      "iter = 23 airway sum = 22027 airway change = 226\n",
      "iter = 24 airway sum = 22095 airway change = 68\n",
      "iter = 25 airway sum = 22129 airway change = 34\n",
      "iter = 26 airway sum = 22129 airway change = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 271.5, 239.5, -0.5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wcul79uyx8PTk++momAeg5FRr5ZmHuH79urxOvT0jeiax9X0DbA79frbf+Z3f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\nLNgJsf8BQlbpQvoWb/IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104a8ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OCNbNUHp8wuxZOrzXVOz4/X5xJrpuwu3n2BiUz+dx+PBhbNy4UbI0Hpv8Pbl/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fAAAD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\nhxZaaKHdJRYCemihhRbaXWL/Dwezbo42pYDEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11042c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####################################################\n",
    "# Initialize parameters \n",
    "#####################################################\n",
    "\n",
    "Radius         = params['airwayRadiusMask']\n",
    "RadiusX        = params['airwayRadiusX']\n",
    "RadiusZ        = params['airwayRadiusZ']\n",
    "mind           = np.argwhere(Mlung == 1)\n",
    "minDiff        = float('inf')\n",
    "initLoc        = [0,0,0];\n",
    "struct_trachea = generate_structure_trachea(Radius, RadiusX, RadiusZ)\n",
    "            \n",
    "#####################################################\n",
    "# Locate an inital point in trachea \n",
    "#####################################################\n",
    "\n",
    "if params['super2infer']:\n",
    "    slice_no  = np.min(mind[:,2])\n",
    "    Itmp      = I[:,:,slice_no:slice_no+RadiusZ]\n",
    "else:\n",
    "    slice_no  = np.max(mind[:,2])\n",
    "    Itmp      = I[:,:,slice_no-RadiusZ:slice_no]\n",
    "    \n",
    "Mtmp = np.ones(Itmp.shape);\n",
    "Mtmp[Itmp < params['lungMinValue']] = 0\n",
    "Mtmp[Itmp > params['lungMaxValue']] = 0\n",
    "Itmp = Mtmp;\n",
    "Mtmp = np.sum(Mtmp, axis = 2)\n",
    "\n",
    "for i in range(Radius, Itmp.shape[0] - Radius):\n",
    "    for j in range(Radius, Itmp.shape[1] - Radius):\n",
    "        if Mtmp[i,j] > 0:   \n",
    "            struct_Itmp = Itmp[i-Radius:i+Radius+1,j-Radius:j+Radius+1,:]\n",
    "            currVal     = struct_Itmp - struct_trachea\n",
    "            currVal     = np.sum(np.square(currVal))\n",
    "            \n",
    "            if currVal  < minDiff:\n",
    "                initLoc = [i,j,slice_no]\n",
    "                minDiff = currVal\n",
    "\n",
    "print 'initial location = '+str(initLoc)\n",
    "\n",
    "#####################################################\n",
    "# Find airway with closed space diallation\n",
    "#####################################################\n",
    "\n",
    "iterNoPerSlice = RadiusX\n",
    "maxFactor      = RadiusX/2\n",
    "maxChange      = RadiusX*RadiusX*RadiusX*50\n",
    "totalChange    = 1\n",
    "tempCheck      = 0\n",
    "\n",
    "Mtmp = np.zeros([m,n,p])\n",
    "if params['super2infer']:\n",
    "    Mtmp[initLoc[0]-Radius:initLoc[0]+Radius+1,\n",
    "         initLoc[1]-Radius:initLoc[1]+Radius+1,\n",
    "         0:slice_no+RadiusZ] = 1\n",
    "else:\n",
    "    Mtmp[initLoc[0]-Radius:initLoc[0]+Radius+1,\n",
    "         initLoc[1]-Radius:initLoc[1]+Radius+1,\n",
    "         slice_no-RadiusZ:p-1] = 1\n",
    "Mtmp  = np.multiply(Mtmp, Mlung)\n",
    "Minit = ndimage.binary_closing(Mtmp, structure = struct_s, iterations = 1)\n",
    "Minit = np.int8(Minit)\n",
    "Minit[Minit > 0] = 2 \n",
    "\n",
    "while totalChange > 0:\n",
    "    \n",
    "    maxSegmentChange = 0;\n",
    "    tempCheck        = tempCheck + 1     \n",
    "    L                = measure.label(np.floor(Minit/2))\n",
    "    Minit[Minit > 1] = 1  \n",
    "                \n",
    "    for label in np.unique(L[:]):\n",
    "        \n",
    "        if label != 0 and np.sum(L[:] == label) > 10:\n",
    "            \n",
    "            # Process each component in local FOV \n",
    "            \n",
    "            xmin, xmax, ymin, ymax, zmin, zmax = bbox2_3D(L,label,iterNoPerSlice,[m,n,p])                                       \n",
    "            Mtmp                = Minit[xmin:xmax,ymin:ymax,zmin:zmax]\n",
    "            Itmp                = I[xmin:xmax,ymin:ymax,zmin:zmax]\n",
    "            Ltmp                = L[xmin:xmax,ymin:ymax,zmin:zmax]\n",
    "            Ltmp[Ltmp != label] = 0\n",
    "            Ltmp[Ltmp > 0]      = 1;\n",
    "\n",
    "            for iterCount in range(0, iterNoPerSlice):\n",
    "                Ltmp = ndimage.binary_dilation(Ltmp, structure = struct_s, iterations = 1)\n",
    "                Ltmp = np.int8(Ltmp)\n",
    "                Ltmp[Itmp > params['lungThreshold']] = 0                \n",
    "\n",
    "            Ltmp = ndimage.binary_closing(Ltmp, structure = struct_s, iterations = 1)\n",
    "            Ltmp = np.int8(Ltmp)\n",
    "            Ltmp[Mtmp > 0] = 0\n",
    "            Ltmp[Ltmp > 0] = 2\n",
    "            Ltmp = Ltmp + Mtmp\n",
    "\n",
    "            segmentChange = np.sum(Ltmp[:]>1)        \n",
    "            if segmentChange < maxChange or tempCheck < 10:\n",
    "                Minit[xmin:xmax,ymin:ymax,zmin:zmax] = Ltmp\n",
    "                if segmentChange > maxSegmentChange:\n",
    "                    maxSegmentChange = segmentChange\n",
    "\n",
    "    if tempCheck < 10:\n",
    "        maxChange = max(maxFactor*maxSegmentChange,maxChange)\n",
    "    else:        \n",
    "        maxChange = min(maxFactor*maxSegmentChange,maxChange)\n",
    "    \n",
    "    totalChange = np.sum(Minit[:] > 1)\n",
    "    \n",
    "    print 'iter = '+str(tempCheck)+' airway sum = '+str(np.sum(Minit[:]>0))\\\n",
    "                        +' airway change = '+str(totalChange)\n",
    "\n",
    "#####################################################\n",
    "# Remove airway & save nii\n",
    "#####################################################\n",
    "\n",
    "Minit[Minit > 0] = 1\n",
    "Minit  = ndimage.binary_opening(Minit, structure = struct_s, iterations = 1)\n",
    "Minit  = ndimage.binary_dilation(Minit, structure = struct_m, iterations = 1)\n",
    "Maw    = np.int8(Minit)\n",
    "Mawtmp = ndimage.binary_dilation(Maw, structure = struct_l, iterations = 1)\n",
    "Mlung[Mawtmp > 0] = 0\n",
    "nib.Nifti1Image(Maw,I_affine).to_filename('./result/sample_aw.nii.gz')\n",
    "nib.Nifti1Image(Mlung,I_affine).to_filename('./result/sample_lung.nii.gz')\n",
    "\n",
    "#####################################################\n",
    "# Display segmentation results \n",
    "#####################################################\n",
    "\n",
    "plt.figure(1)\n",
    "slice_no = int(n*0.45)\n",
    "plt.subplot(121)\n",
    "plt.imshow(np.fliplr(np.rot90(I[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.fliplr(np.rot90(Maw[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "\n",
    "plt.figure(2)\n",
    "slice_no = int(n*0.45)\n",
    "plt.subplot(121)\n",
    "plt.imshow(np.fliplr(np.rot90(I[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.fliplr(np.rot90(Mlung[:,slice_no,:])), cmap = plt.cm.gray)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}