mask_generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "077f936f",
   "metadata": {},
   "source": [
    "# Functions for Generating Custom Masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f155fd81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import SimpleITK as sitk\n",
    "from scipy.ndimage import shift\n",
    "from scipy.ndimage import map_coordinates\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "from multiprocessing import Pool\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdaf3b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_directory = \"dataset/whole_head/mask/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e288ee4",
   "metadata": {},
   "source": [
    "### 1. Scale tumor mask relative to tumor center within head mask area, with specified range and number of output images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c74347c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 27.55it/s]\n"
     ]
    }
   ],
   "source": [
    "def scale_tumor_part(args):\n",
    "    \"\"\"\n",
    "    Scales the tumor part of a given mask by a specified scale factor.\n",
    "\n",
    "    :param args: Tuple containing (output_directory, mask_path, scale).\n",
    "    \"\"\"\n",
    "    output_directory, mask_path, scale = args\n",
    "    name = os.path.basename(mask_path)\n",
    "    img = sitk.ReadImage(mask_path)\n",
    "    img_array = sitk.GetArrayFromImage(img)\n",
    "\n",
    "    tumor_mask = img_array == 2\n",
    "    tumor_positions = np.argwhere(tumor_mask)\n",
    "    center_of_tumor = tumor_positions.mean(axis=0)\n",
    "\n",
    "    coords = np.indices(img_array.shape).reshape(3, -1)\n",
    "    coords_shifted = coords - np.expand_dims(center_of_tumor, axis=1)\n",
    "\n",
    "    scaled_coords = coords_shifted / scale + np.expand_dims(center_of_tumor, axis=1)\n",
    "    transformed_tumor = map_coordinates(tumor_mask, scaled_coords, order=0, mode='constant').reshape(img_array.shape)\n",
    "\n",
    "    transformed_tumor_mask = np.round(transformed_tumor).astype(int).clip(0, 1)\n",
    "    brain_mask = img_array == 1\n",
    "    brain_mask[img_array == 2] = 1\n",
    "    final_mask = brain_mask + 2 * transformed_tumor_mask\n",
    "    final_mask[final_mask == 3] = 2\n",
    "\n",
    "    val, counts = np.unique(final_mask, return_counts=True)\n",
    "    if len(counts) >= 3 and counts[2] >= 30:\n",
    "        final_mask_img = sitk.GetImageFromArray(final_mask)\n",
    "        final_mask_img.CopyInformation(img)\n",
    "        sitk.WriteImage(final_mask_img, os.path.join(output_directory, f\"{scale}_{name}\"))\n",
    "\n",
    "def process_masks(input_directory, output_directory, scale_start, scale_end, num_scales):\n",
    "    if not os.path.exists(output_directory):\n",
    "        os.makedirs(output_directory)\n",
    "\n",
    "    scales = np.linspace(scale_start, scale_end, num_scales)\n",
    "    mask_list = sorted(glob.glob(os.path.join(input_directory, \"*.nii.gz\")))\n",
    "\n",
    "    # Prepare arguments for parallel processing\n",
    "    args = [(output_directory, mask, scale) for mask in mask_list for scale in scales]\n",
    "\n",
    "    # Use multiprocessing pool to process images in parallel\n",
    "    with Pool() as pool:\n",
    "        list(tqdm(pool.imap(scale_tumor_part, args), total=len(args)))\n",
    "\n",
    "scale_start = 2.0\n",
    "scale_end = 0.5\n",
    "num_scales = 20\n",
    "output_directory = \"dataset/scaled_masks\"\n",
    "process_masks(input_directory, output_directory, scale_start, scale_end, num_scales)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cda908",
   "metadata": {},
   "source": [
    "#### Largest tumor slice in original image for axial plane comparative plotting with all scaled images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c036c77c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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ss0+88MILMWXKlGqfecYZZ8R6660Xhx122Eq3syaLu5xbstvxN954I/7+97/HL37xi+jevXvMmTOnWheki1VWVsYqq6wSpaWluWFt2rSJVVddNW/ZF/vyyy/jrLPOivPPP7/G73dE1Ni9z/777x8REa+//nre8L59++Z+nV+bv/zlL9G6deu8u7Tbt28fRx99dIwfP77GngMWLFhQrTuf5WndunX07du3xm7cgWxradkdUfd9cG3qkguL/eY3v4mqqqr4+c9/Xuf5/+1vf4uZM2fWKRN79OgRHTt2zNuHz5gxI0aPHp3rLnXevHk13rkdsSgT27dvX205evfuXWMmzp8/P0466aQ46aSTYu21165xnttvv321dbz99ttHt27dqmVinz59om3btstdzvvvvz/mz58fP/3pT3PDSkpK4ic/+Ul8+OGHNf7qf3nHTZtvvnm17l67d+8e3/nOd6q184orrohevXrFSSedFCmlemdtTccuXbp0iW7dui33vVVVVXHllVfG/vvvH1tttVUsWLCgWvf2QMsiv+uvf//+eee+9dGxY8dYbbXVqp0v1TXDljR79uyYN29enaefO3du/PWvf40ddtghr/ePxfnWs2fPvOl79+4dEZHL8PHjx8eCBQuq9Zyy+O+lu2aPiJgyZUpcfvnlcdlll+Vd61iRdi7LmmuuGV999VXeuqhr1tZ12Ze0vOOxQYMGxaqrrpo3rLS0NPbcc8/48MMPY+bMmbnhdc3vVVddNQYNGlRt+LKuEwDZJrvrpzH2y5WVlfHYY4/FYYcdFmVlZbnhhx9+eHTu3Dn+/Oc/1/i+mTNnLvMxqPXZ169ILWBJNZ1TRqzYMc7MmTPr9dkTJ06M//73vzXWF+pyPeHpp5+OiKjxeGTOnDlx//33V/vMP/3pTzF58uRqXbGvSDtrsqzju6V98skn8cQTT8QBBxxQ453pFCeFcFZY//79Y9KkSct8TuWSzjvvvPjRj34Ubdu2jfPPPz/OO++86Nu3b95zn2699dbo3LlznHrqqXHllVfG5ptvHuecc06cccYZy53/iBEj4he/+EVsu+22ceWVV8aRRx4ZY8aMid122y2vOHrOOefE2WefHZtssklceumlsdZaa8Wuu+663C5OIxZ1URIRscUWW+QN79OnT6yxxhq58cuy+JkmpaWlscUWW0SnTp2iY8eOccghh8SXX35ZbfqvvvoqysrKory8PLp16xbHHXdcnS7CjhkzJvr27VutC/Mdd9wxHn744bj66qvjvffeizfeeCOOO+64qKioiJNOOik33dy5c2s8kVyyS9UlTZw4MW677ba44oor6hXCy2rnYl988UV8+umn8fzzz+e6v1myu9jF67Nnz56xyy67RIcOHaJDhw6xxx57VHt+yo477hivvvpqnH322fHf//433n777bjgggvi+eefj9NOO63aZ5999tnRq1evGDFiRJ2XJyJyz1dZ+sCtrl588cVYd9118w7OIiK22mqriFjUnc6S3nrrrejUqVN06dIlevXqFWefffYyfwwwe/bs+Pzzz+Ptt9+Oyy+/PP7+97/nrU+gZWhp2d1Q6poLH3zwQVxyySXx61//usYsXZYxY8ZEhw4dco8pWdqMGTPis88+i1deeSX+7//+LyorK/P24c8880zMmTMn1llnnTjooIOiY8eO0aFDh9h2222rZceOO+4YlZWVMWLEiHj99dfj/fffjxtuuCHuvffeOPPMM6t99hVXXBH/+9//4qyzzqrz8kQs6mJ11qxZK5WJnTp1ig022CBv+OJMXPq4a0WPmyIW5ffS7Rw3blxsueWWcdVVV8Vqq62We6b4NddcU+M85s2bF59//nlMnTo1xo4dG7/97W+jf//+NT4aZnlee+21+Pjjj2PjjTeOY489Njp16hSdOnWKjTfeOJ544ol6zw8ofvK78VVWVsbnn38eb7zxRvzyl7+MyZMnr/T50nnnnRedO3eO9u3bx5ZbbplX0FiWhx56KGbMmFHtgu7iH52ddNJJ8e9//zs+/PDDeOihh+Kiiy6K/fbbL9Zff/2IiNwP4ZY+DlnW+XxExMknnxw77bRTtW7cV6Sdi3399dfx+eefx3vvvRe33XZbjB49OoYMGZLXrrpm7eLrJ2effXY8/vjj8dFHH8WTTz4Zp512Wmy55ZbVHm2yosdjEYuOCTp27JhbXw1hZa8TAMVJdjeMldkvv/LKK7FgwYJq1/LbtWsXm266aY3X8o888sgoKyuL9u3bx0477RTPP/98ndsZkb+vr28tIGL518NXxE477RRlZWW5G91qusltaWPGjImIqJbzdb2eMHfu3GjdunW0a9cu7/3LOh6ZOXNmnH766fHLX/4y79FqK9rOxVbk+O6uu+6KqqqqOhXXKSIFuxedovfoo4+m1q1bp9atW6chQ4ak0047LT3yyCO5rsIWmzJlSmrVqlXaf//9q3WbuWQ33TV1ETJixIjUsWPHvC6tl+7i5emnn04RkcaMGZP33ocffjhv+KeffpratWuX9tprr7zP/eUvf5kiYrldvFx66aUpItIHH3xQbdyWW26Ztt5661rfv88++6SISN27d0/Dhg1Lf/nLX9LZZ5+d2rRpk7bZZpu8Np1xxhnp9NNPT3fffXe688470/Dhw1NEpG233TavW/alTZ48OUVEOu2006qNmz59etpll11SROReq666anr22Wfzptt7771T165dU2VlZd7wIUOGpIhIv/3tb3PDqqqq0lZbbZUOPfTQlFLdu4CrrZ2LlZaW5trZvXv3dNVVV+WNP/HEE3Pjdt9993T33XenSy+9NHXu3Dmtvfbaafbs2blpZ82alb7//e+nkpKS3Dw7duyY7rvvvmqf+/LLL6fWrVvnusc599xza+0Cd0lHH310at26dXrrrbeWOU1tXQMNGjQo7bzzztWGv/rqqyki0g033JAbdtRRR6WRI0emv/71r+mPf/xj7vv1/e9/v8Z5jxgxIrfsrVq1SgcddFD68ssvl7tMQLa0tOxe2op0rVqfXDjooIPSNttsk/s76tA1+hdffJHatWu3zP13Simtt956uX14586d01lnnZX3/3LZZZflMnGrrbZKY8aMSdddd13q2bNnWmWVVdLHH3+cm3bBggXp+OOPT23bts3Ns3Xr1un666+v9rmffPJJ6tKlS7rxxhtTSvXrWu2CCy5IEZHGjRu3zGlq61Z2r732SmuttVa14bNnz04Rkc4444zcsBU9bkoppaeeeiqVlJSks88+Ozfsyy+/zK3Pzp07p0svvTTdfffdaffdd6+Wx4vdeeedecdYW2yxRfrPf/6zzM+trQu/e++9N/f5AwcOTKNHj06jR49OAwcOTO3atUsvv/xyrcsEZI/8XvFHm6RUt67Rd9ttt9w+vF27dmnEiBG1dodZW4a9//77adddd03XX399euCBB9IVV1yR+vXrl1q1alWta/OlHXjggam0tLTGrnT/8Ic/pK5du+blzfDhw/OybtKkSSki0gUXXJD33sX/R507d84b/uCDD6Y2bdqkV199NaVU98eN1NbOlFIaNWpUXjt32WWXvGsp9c3aBx98MPXu3Ttvnrvttlu1R56ktGLHYykt2n7at29frVv5JS2vC96lzZ07N33rW99KAwYMWO4xCZAtsnvlsjulld8vLx731FNPVRt38MEHp169euX+/te//pUOPPDAdPPNN6f7778/jRo1KnXv3j21b98+9/iRZVnWvr4+tYDFlnc9fGm1HePcfffd6Ygjjki33XZbGjt2bDrrrLNSx44d06qrrlpjfWOxBQsWpJ49e6atttqqxnF1uZ7wu9/9LkVEevrpp/OGn3HGGSki0ve+97284T//+c/TgAEDct/lunSNXls7F6vv8V1KKW2++eapd+/e1bZHiptCOCtl4sSJaf/9908dO3bM7VRWW221dP/99+emWVxAfvHFF+s838rKyvTZZ5+l22+/PUVEeumll3Ljlg70E088MZWXl6dPP/00ffbZZ3mvzp07p//7v/9LKaV0xx13pIhIDz/8cN5nffrpp3UK9MXPlqrpGRbf+c530iabbFLr+3feeecUEWn33XfPG774BPGxxx6r9f0XXXRRioh05513LnOaM888M0VEjRdIZ86cmX7605+m4cOHp3vuuSfdcsstaaONNkq9evXKe37JQw89lCIi7bHHHumFF15Ib775ZjrppJNyAbfkCfUtt9ySOnTokAvPuhbCa2vnYo8//nh66KGH0u9+97u02WabVXseylFHHZUiIg0aNCgvmBZfiF7yOd3z589PZ511Vjr44IPTnXfemW6//fa0/fbbp86dO6fx48fnzXeHHXbIC+O6FsIXP7OttuJ+SrUfCK611lppjz32qDb87bffThGRLr/88lrnfcwxx6SIqLZMKaX0+uuvp8ceeyzddtttaa+99kr7779/mjZtWq3zA7KpJWX30lbkZLyuufD444+nkpKSNHHixNywulx4vfHGG1NE5K3/pT377LPp4YcfTtddd13acsst089+9rO8CyiLj1FWXXXVvAvC48ePTxHVn9N9+eWXp+9973vptttuS3fffXfab7/9Ups2bdLYsWPzpjv88MPTJptsksvZuhbCn3zyydSmTZtai/sp1V5E2HnnndMGG2xQbfjChQtTRKSTTjqp1nnX5bhp+vTpaY011khrrbVW3nr74IMPctvGXXfdlffZ3/rWt6o9oy6lRc9uf+yxx9I999yTfvzjH6chQ4bUmMeL1XbB5o9//GPuRH3JCxTvv/9+atu2bRo2bFityw5kk/zeoV7vWVJdCuEvvvhievTRR9PNN9+ctt9++3TkkUfWWGRdrLYMq8kXX3yRevbsmdZbb71lTlNRUZHat2+f9t9//xrH//3vf0+77rpruuKKK9LYsWPTqaeemtq0aZN+9rOf5U03ePDg1Llz53TLLbekd999Nz300EOpf//+qW3btql169a56ebOnZsGDhyYjj/++NywuhTCl9fOlBY9v/Sxxx5Ld9xxR/rhD3+Ydtlll/Tmm2/mxtc3aydMmJD23HPPdNFFF6X77rsvjRw5MnXs2DEddNBBedOt6PHY7Nmz06abbppWWWWV9NFHHy1zuvoWwhefo/+///f/6jQ9kC2ye4d6vWdJDbFfXnxeNWHChGrjfvSjH6Xy8vJa2zBlypTUoUOH5T5Teln7+hWpBSzvevjS6nKMs6Snn346lZSUpBEjRixzmkceeSRFRLryyitrHF+X6wmffPJJKi8vTwMHDkyPPvpoevfdd9ONN96YysrKcj+QW+zNN99Mbdu2TX/5y19yw+pSCF9eO1Oq//Hdm2++mSIinXLKKbV+NsVHIZwGMXfu3DRx4sR05plnpvbt26e2bdvmflH84x//OLVq1SrNnTu31nlMnjw57bfffrkd4pKvJ598Mjfd0oG+xx57VJt+ydc+++yTUvomZN5+++1qn73KKqs0+h3he+21V4qIdNttt+UNf//991NEpPPOO6/W93/11VepVatW6eijj65xfFVVVerfv3/acMMNaxy/++67V/u11RdffJG6detW7UL11VdfnTp16pRbh+uss076zW9+k1eMraioSD179kznnHNO7n11KYQvr501+e9//5vat2+frr766tyw4447rsb1tmDBgtSmTZt05JFH5oaNGDEi70J+SinNmzcvDRw4MO9XY3fddVdq27Zt3gl6XQrhTz31VGrfvn3abbfdlvsr74a6I7wmb7zxRo2//q/Jd7/73bTlllvW+OtDoGVoCdm9tPqejNc1F+bPn5823HDDdPjhh+e9vy4XXrfffvvUrVu3ancGLMuXX36ZevbsmXfRe/ExypLZt9iAAQPSTjvtlPt71KhRqVevXtVO/nbcccfUp0+fXI6NHz8+lZSUpMcffzw3TV0K4a+//nrq1q1b2nTTTav1LrO0hrojvCbLO26aNWtW2nLLLVN5eXl65ZVX8sZ99tlnKSJS27Zt04IFC/LGnXfeeSki0vvvv1/r51900UWpc+fO6ZNPPqlxfF3uXFjy/22xnXbaKQ0YMKDWzwayTX7XX30vEs+dOzcNGjQoHXjggcucpr6F8JS+uQtq6tSpNY6/5ZZbUkTkXQhe7JlnnkmtW7eulsEjR45MJSUlue9ASil9+OGHadttt839v7Ru3Tr94he/SFtttVXeRf9LLrkkrbLKKumLL77IDatLIby2di7LMccck/r27Zu7o7E+Wfv222+njh07Vvu8W2+9NUVEeuihh1JKK348tmDBgrT33nundu3a1dqTTUr1K4QvvoZSl/NzINtkd/001H65PneEL8shhxyS2rVrVy2rFqttX7+ytYCarocvrb7HOCmltPXWW6e11157meMPP/zw1Lp16xpvoKrr9YSUFv1Avl+/frnvWllZWbrttttSRKR99903N93uu+9e7btSl0J4be2sSV2O784555wUEen555+v0zwpHm0CGkC7du1iyy23jC233DLWXXfdOPLII+Oee+6Jc889t07vnzFjRuywww5RVlYW559/fqy99trRvn37eOGFF+L000+PqqqqZb63qqoqevTokXsmxNJWW221FVqmpfXu3TsiIj755JPo27dv3rhPPvkk98zKZenTp09ELHqm9ZJ69OgRERH/+9//an1/hw4donv37st8hsi//vWveP/992PUqFHVxr3zzjvx8MMPx0033ZQ3vFu3brHddtvFv/71r7zhxx9/fBx55JHxn//8J/fclJtvvjkiItZdd92IiPjtb38b8+bNix/84Ae5Z3J/+OGHuWV57733ok+fPtWeBVJbO5dl7bXXjs022yzGjBkTxx9/fEQse322bt06unfvnluf8+bNi5tvvjlOO+20aNWqVW66tm3bxh577BHXXHNNzJs3L9q1axe/+MUv4uCDD4527drllmnGjBkRETF16tSYN29e7nMXe/nll2OfffaJDTfcMP7yl79EmzYrvlvt3bt3fPTRR9WGf/LJJ3nLvCyLv5fL+o4s6aCDDooRI0bEW2+9Feutt94KtBYodi0hu1dWXXPhj3/8Y7z55ptx44035qZbbObMmfHee+9Fjx49qj3X7IMPPoinn346jj322Gjbtm2d2rTKKqvEzjvvHGPGjInf/va3EbHsTIxYdJyx5DHGddddFzvvvHN07tw5b7p99tknTj311HjvvfdinXXWidNOOy2+853vxIABA3LL9Pnnn0fEolz64IMPol+/fnnzmDp1auy6665RXl4eDz30UHTp0qVOy1ST3r17xxNPPBEppSgpKckNr2sm1nbcNG/evDjggAPiP//5TzzyyCOx4YYb5o3v1q1btG/fPrp27RqtW7fOG7fkcdvSy7+kgw46KH71q1/F/fffv9xnyy9tef+fNT3LDmg55Hfja9euXeyzzz5xySWXxNdff13v50wvy5Lna2ussUa18WPGjIny8vL43ve+V23cjTfeGD179qz2nNN99tknRo4cGc8++2x861vfioiI1VdfPZ555pmYMmVKTJs2LQYOHBi9evWKPn365M7nKyoq4sILL4yf/vSnUVlZGZWVlRERMWvWrEgpxXvvvRcdO3bM5V5d27ksBx10UPz+97+Pp556Knbbbbd6Ze2tt94ac+bMqfZ5++yzT0Qsusawxx57rPDx2DHHHBMPPvhgjBkzJnbeeec6L1Ntbr311jj99NPjxz/+cZx11lkNMk+geMnu+mmo/fKS1/KX9sknnyz3nDJiUXbPmzcvZs+eHWVlZXnjlrevX9laQE3XwxtC3759480336xx3Ndffx1jx46NoUOH1ng+WtfrCRER22+/fbzzzjvxyiuvxOzZs2OTTTaJjz/+OCK+qS88/vjj8fDDD8e9996bl90LFiyIr7/+Ot57773o1q1btXW/vHbWpC7Hd3fccUest956sfnmm9dpnhQPhXAa3OITs8Uhs/baa0dVVVW89tprsemmm9b4nn/+85/xxRdfxL333hvbb799bvi777673M9be+214x//+Edsu+22tZ6g9u/fPyIipkyZEmuttVZu+Geffbbc4ImIXNuff/75vKL3xx9/HB9++GEce+yxtb5/8803j9///vfVCp2LA2B5Bx4zZ86Mzz//fJnTjRkzJkpKSuKHP/xhtXHTp0+PiIiFCxdWGzd//vxYsGBBteGdOnWKIUOG5P7+xz/+ER06dIhtt902IhZdvP/f//4XgwYNqvbeiy++OC6++OJ48cUXq/2f19bO2nz99dcxd+7c3N+LA2np9Tlv3ry89fTFF1/EggULlrnsVVVVuXFTp06NO+64I+64445q037729+OTTbZJF566aXcsLfffjt233336NGjRzz00EPVDgLqa9NNN40nnngiKisr8wJ+woQJufG1eeeddyKibgexX3/9dUQsugACkNXsXll1zYUPPvgg5s+fn8vIJf3xj3+MP/7xjzF27NjYb7/98sbdeeedkVKKYcOG1atdX3/9dd7+e1mZGLHoOGP99dfP/T19+vRlZmJE5I4JPvjgg3j//fdjwIAB1abdZ599ory8PPejgIhFebvrrrvG3LlzY9y4cbmLDitq0003jT/84Q/x+uuv5y7sR9Q9E5d13FRVVRWHH354jBs3Lv785z/HDjvsUO29rVq1ik033TSee+653I/lFqvrcdvK5OxGG20Ubdu2Xeb/Z3O8WAUUhvxuPF9//XWklGLmzJkNVgiv7Xztk08+iSeeeCKOOOKIKC0trTa+rvm9pIEDB8bAgQMjIuK1116LTz75JI444oiIWHTxfdasWfGb3/wmfvOb31R774ABA2LfffeN++67r17tXJalc7E+WTt9+vRIKVVb/pqOXep7PPaLX/wiRo8eHVdccUUceuihdV6e2tx///3xf//3f3HAAQfEtdde2yDzBLJDdteuIffLG264YbRp0yaef/75+P73v58bPm/evHjppZfyhi3LO++8E+3bt692zbcu+/qVrQVEVL8e3hDeeeedZX72Aw88EDNnzlzmNYr6Ho+0bt0673v9j3/8IyIihg4dGhGLsjsi4oADDqg2z48++igGDBgQl19+eZx88sn1auey1HZ8N2HChPjvf/8b559/fr3mSXFotfxJoGaL79JZ2kMPPRQRkbvLdL/99otWrVrF+eefX+0Xaovfv/gXwEvOb968eXHdddcttx3f//73Y+HChXHBBRdUG7dgwYLcRdqhQ4dG27Zt4+qrr877nCuuuGK5nxERMWjQoFh//fXjpptuytvhX3/99VFSUhIHHXRQblhFRUW88cYbeRc/99133ygtLY3Ro0fnrYc//OEPERHx3e9+NyIi5syZEzNnzqz2+RdccEGklGL33XevNm7+/Plxzz33xHbbbVfj3UnrrLNOtGrVKu6+++68Zf/www/j6aefjs0226zWZX/22Wfj3nvvjaOPPjrKy8sjIuLEE0+MsWPH5r1uvPHGiIg44ogjYuzYsdUuni+vnQsWLKjx4GrixInxyiuv5P36fccdd8z9onHOnDm54bfeemssXLgwtz579OgRXbt2jbFjx8a8efNy082aNSv+9re/xfrrr58LvqWXZ+zYsfGDH/wgIhadOF9++eW590+bNi123XXXaNWqVTzyyCMNclH6oIMOioULF+bduT937twYPXp0DB48OHcHQWVlZbWDoJRSXHjhhRERsdtuu+WGf/rpp9U+Z/78+fHHP/4xOnTokFdcALKvpWV3fXz11Vfxxhtv5O56jqh7LhxyyCE1ThsRseeee8bYsWNj8ODB1T7zjjvuiH79+sV2221XY5tq2oe/9957MW7cuLxMXG+99WKTTTaJ+++/P6/9jz76aEydOjWXiRGLfnn92GOPxRdffJEbtnDhwvjzn/8cXbp0ibXXXjsiIm666aZqy3PCCSdExKJeYZa8o2D27Nmx5557xkcffRQPPfRQ7qL7yth3332jbdu2ed+nlFLccMMNsfrqq8c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      "text/plain": [
       "<Figure size 2000x3000 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_largest_tumor_slice(img_array):\n",
    "    \"\"\"\n",
    "    Find the index of the slice with the largest tumor area.\n",
    "\n",
    "    :param img_array: 3D numpy array of the image.\n",
    "    :return: Index of the slice with the largest tumor.\n",
    "    \"\"\"\n",
    "    tumor_areas = np.sum(img_array == 2, axis=(1, 2))  # Assuming tumor label is 2\n",
    "    return np.argmax(tumor_areas)\n",
    "\n",
    "def plot_largest_tumor_slices(original_mask_path, scaled_masks_dir):\n",
    "    \"\"\"\n",
    "    Plot the largest tumor slice of the original and scaled images in a grid layout with 4 columns.\n",
    "\n",
    "    :param original_mask_path: Path to the original mask file.\n",
    "    :param scaled_masks_dir: Directory containing scaled mask files.\n",
    "    \"\"\"\n",
    "    # Load original mask and find largest tumor slice\n",
    "    original_img = sitk.ReadImage(original_mask_path)\n",
    "    original_img_array = sitk.GetArrayFromImage(original_img)\n",
    "    largest_slice_original = find_largest_tumor_slice(original_img_array)\n",
    "\n",
    "    # Fetch all scaled mask paths\n",
    "    scaled_mask_paths = glob.glob(os.path.join(scaled_masks_dir, f\"*_{os.path.basename(original_mask_path)}\"))\n",
    "    \n",
    "    # Calculate number of rows for the subplot grid\n",
    "    num_images = 1 + len(scaled_mask_paths)\n",
    "    num_rows = (num_images + 3) // 4  # Ensure at least one row and calculate required number of rows\n",
    "\n",
    "    # Create subplots with 4 columns and calculated number of rows\n",
    "    fig, axes = plt.subplots(num_rows, 4, figsize=(20, 5 * num_rows))\n",
    "    axes = axes.flatten()  # Flatten the axes array for easy indexing\n",
    "\n",
    "    # Plot original image\n",
    "    axes[0].imshow(original_img_array[largest_slice_original], cmap='gray')\n",
    "    axes[0].set_title(\"Original\")\n",
    "\n",
    "    # Plot each scaled image\n",
    "    for i, scaled_mask_path in enumerate(scaled_mask_paths):\n",
    "        scaled_img = sitk.ReadImage(scaled_mask_path)\n",
    "        scaled_img_array = sitk.GetArrayFromImage(scaled_img)\n",
    "        largest_slice_scaled = find_largest_tumor_slice(scaled_img_array)\n",
    "\n",
    "        axes[i + 1].imshow(scaled_img_array[largest_slice_scaled], cmap='gray')\n",
    "        scale_factor = os.path.basename(scaled_mask_path).split(\"_\")[0]  # Assuming filename format is 'scale_factor_filename'\n",
    "        axes[i + 1].set_title(f\"Scaled {scale_factor}\")\n",
    "\n",
    "    # Hide any unused axes\n",
    "    for j in range(i + 2, len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "mask_list = sorted(glob.glob(os.path.join(input_directory, \"*.nii.gz\")))\n",
    "scaled_masks_dir = \"dataset/scaled_masks/\"\n",
    "plot_largest_tumor_slices(mask_list[0], scaled_masks_dir)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f1cc5a",
   "metadata": {},
   "source": [
    "### 2. Shift tumor mask in x, y, and z directions within head mask area, with specified range and step size for shifting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2ab328c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:17<00:00, 17.75s/it]\n"
     ]
    }
   ],
   "source": [
    "output_directory = \"dataset/shifted_masks\"\n",
    "\n",
    "if not os.path.exists(output_directory):\n",
    "    os.makedirs(output_directory)\n",
    "\n",
    "def process_shift(args):\n",
    "    mask, x_shift, y_shift, z_shift, name, brain_mask = args\n",
    "    img = sitk.ReadImage(mask)\n",
    "    img_array = sitk.GetArrayFromImage(img)\n",
    "    original_tumor_mask = img_array == 2\n",
    "    brain_mask = img_array == 1\n",
    "\n",
    "    # Shift the tumor mask\n",
    "    shifted_tumor_mask = shift(original_tumor_mask, [x_shift, y_shift, z_shift], mode='constant')\n",
    "    \n",
    "    # Ensure the shifted tumor stays within the brain area\n",
    "    shifted_tumor_within_brain = np.logical_and(shifted_tumor_mask, brain_mask)\n",
    "\n",
    "    # Check if at least half of the tumor is still within the brain\n",
    "    original_tumor_area = np.sum(original_tumor_mask)\n",
    "    shifted_tumor_within_brain_area = np.sum(shifted_tumor_within_brain)\n",
    "    if shifted_tumor_within_brain_area < original_tumor_area / 2:\n",
    "        return  # Skip further processing if more than half of the tumor is outside the brain\n",
    "\n",
    "    # Fill the original tumor position with brain tissue\n",
    "    img_array[original_tumor_mask] = 1\n",
    "\n",
    "    # Overlay the shifted tumor onto the brain\n",
    "    final_mask = np.where(shifted_tumor_within_brain, 2, img_array)\n",
    "    final_mask_img = sitk.GetImageFromArray(final_mask)\n",
    "    final_mask_img.CopyInformation(img)\n",
    "\n",
    "    sitk.WriteImage(final_mask_img, f\"{output_directory}/{x_shift}_{y_shift}_{z_shift}__{name}\")\n",
    "\n",
    "def shiftTumor(mask, shift_range, step):\n",
    "    name = mask.split(\"/\")[-1]\n",
    "    img = sitk.ReadImage(mask)\n",
    "    img_array = sitk.GetArrayFromImage(img)\n",
    "\n",
    "    brain_mask = img_array == 1\n",
    "\n",
    "    shift_args = [(mask, x_shift, y_shift, z_shift, name, brain_mask)\n",
    "                  for x_shift in range(-shift_range, shift_range + 1, step)\n",
    "                  for y_shift in range(-shift_range, shift_range + 1, step)\n",
    "                  for z_shift in range(-shift_range, shift_range + 1, step)]\n",
    "\n",
    "    with Pool() as pool:\n",
    "        pool.map(process_shift, shift_args)\n",
    "\n",
    "mask_list = sorted(glob.glob(f\"{input_directory}/*.nii.gz\"))\n",
    "for mask_path in tqdm(mask_list):\n",
    "    shiftTumor(mask_path, 40, 10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfc2ef3",
   "metadata": {},
   "source": [
    "#### Largest tumor slice comparison between each shifted image and original image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "259a7d6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x6500 with 52 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_largest_tumor_slice(img_path):\n",
    "    \"\"\"\n",
    "    Find the index of the slice with the largest tumor area in the given image file.\n",
    "\n",
    "    :param img_path: Path to the image file.\n",
    "    :return: Index of the slice with the largest tumor.\n",
    "    \"\"\"\n",
    "    img = sitk.ReadImage(img_path)\n",
    "    img_array = sitk.GetArrayFromImage(img)\n",
    "    tumor_areas = np.sum(img_array == 2, axis=(1, 2))  # Assuming tumor label is 2\n",
    "    return np.argmax(tumor_areas)\n",
    "\n",
    "def plot_largest_tumor_slice(ax, img_path, title):\n",
    "    \"\"\"\n",
    "    Plot the largest tumor slice of an image on the given axes.\n",
    "\n",
    "    :param ax: Matplotlib axes to plot on.\n",
    "    :param img_path: Path to the image file.\n",
    "    :param title: Title for the plot.\n",
    "    \"\"\"\n",
    "    img = sitk.ReadImage(img_path)\n",
    "    img_array = sitk.GetArrayFromImage(img)\n",
    "    largest_slice = find_largest_tumor_slice(img_path)\n",
    "    ax.imshow(img_array[largest_slice], cmap='gray')\n",
    "    ax.set_title(title)\n",
    "\n",
    "def plot_largest_tumor_slices(original_mask_path, scaled_masks_dir):\n",
    "    \"\"\"\n",
    "    Plot the largest tumor slice of the original and scaled images in a grid layout with 4 columns.\n",
    "\n",
    "    :param original_mask_path: Path to the original mask file.\n",
    "    :param scaled_masks_dir: Directory containing scaled mask files.\n",
    "    \"\"\"\n",
    "    # Fetch all scaled mask paths\n",
    "    scaled_mask_paths = glob.glob(os.path.join(scaled_masks_dir, f\"*_{os.path.basename(original_mask_path)}\"))[:50]\n",
    "\n",
    "    # Calculate number of rows for the subplot grid\n",
    "    num_images = 1 + len(scaled_mask_paths)\n",
    "    num_rows = (num_images + 3) // 4  # Ensure at least one row and calculate required number of rows\n",
    "\n",
    "    # Create subplots with 4 columns and calculated number of rows\n",
    "    fig, axes = plt.subplots(num_rows, 4, figsize=(20, 5 * num_rows))\n",
    "    axes = axes.flatten()  # Flatten the axes array for easy indexing\n",
    "\n",
    "    # Plot original image\n",
    "    plot_largest_tumor_slice(axes[0], original_mask_path, \"Original\")\n",
    "\n",
    "    # Plot each scaled image\n",
    "    for i, scaled_mask_path in enumerate(scaled_mask_paths):\n",
    "        scale_factor = os.path.basename(scaled_mask_path).split(\"__\")[0]  # Assuming filename format is 'scale_factor_filename'\n",
    "        plot_largest_tumor_slice(axes[i + 1], scaled_mask_path, f\"{scale_factor}\")\n",
    "\n",
    "    # Hide any unused axes\n",
    "    for j in range(i + 2, len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "mask_list = sorted(glob.glob(os.path.join(input_directory, \"*.nii.gz\")))\n",
    "shifted_masks_dir = \"dataset/shifted_masks/\"\n",
    "plot_largest_tumor_slices(mask_list[0], shifted_masks_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d225d4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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