UW-Madison GI Tract Image Segmentation - EDA.ipynb

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   "source": [
    "from tqdm.notebook import tqdm\n",
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
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   "source": [
    "## UW-Madison GI Tract Image Segmentation"
   ]
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    "## 1. Introduction\n",
    "\n",
    "This competition's objective is segmentation of stomach and intestines on MRI scans. The MRI scans are from actual cancer patients who had 1-5 MRI scans on separate days during their radiation treatment. There are total of 85 cases, 274 MRI scans and 33913 annotations in training set, and there are roughly 50 cases in hidden test set.\n",
    "\n",
    "There are 3 columns in `train.csv` file named id, class and segmentation.\n",
    "\n",
    "* `id` - Unique ID of the slice\n",
    "* `class` - Class of the object (large bowel, small bowel or stomach)\n",
    "* `segmentation` - Run length encoded segmentation masks\n",
    "\n",
    "id column can be decomposed into 3 columns; case, day and slice. Those columns can be used for selecting slices from directories.\n",
    "\n",
    "* `case` - Case ID\n",
    "* `day` - Scan day\n",
    "* `slice` - Slice number"
   ]
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     "text": [
      "Training Set Shape: (115488, 3) - 33913 Annotations - Memory Usage: 2.64 MB\n"
     ]
    }
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   "source": [
    "df_train = pd.read_csv('../input/uw-madison-gi-tract-image-segmentation/train.csv')\n",
    "print(f'Training Set Shape: {df_train.shape} - {df_train[~df_train[\"segmentation\"].isnull()].shape[0]} Annotations - Memory Usage: {df_train.memory_usage().sum() / 1024 ** 2:.2f} MB')\n",
    "\n",
    "df_train = pd.concat((\n",
    "    df_train,\n",
    "    df_train['id'].str.split('_', expand=True).drop(columns=[2]).rename(columns={0: 'case', 1: 'day', 3: 'slice_number'})\n",
    "), axis=1)"
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   "source": [
    "Training annotations are provided as run-length encoded binary segmentation masks. They have to be decoded into 2 dimensional arrays in order to be processed which can be done with the function defined below."
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   "source": [
    "def decode_rle_mask(rle_mask, shape):\n",
    "\n",
    "    \"\"\"\n",
    "    Decode run-length encoded segmentation mask string into 2d array\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    rle_mask (str): Run-length encoded segmentation mask string\n",
    "    shape (tuple): Height and width of the mask\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    mask [numpy.ndarray of shape (height, width)]: Decoded 2d segmentation mask\n",
    "    \"\"\"\n",
    "\n",
    "    rle_mask = rle_mask.split()\n",
    "    starts, lengths = [np.asarray(x, dtype=int) for x in (rle_mask[0:][::2], rle_mask[1:][::2])]\n",
    "    starts -= 1\n",
    "    ends = starts + lengths\n",
    "\n",
    "    mask = np.zeros((shape[0] * shape[1]), dtype=np.uint8)\n",
    "    for start, end in zip(starts, ends):\n",
    "        mask[start:end] = 1\n",
    "\n",
    "    mask = mask.reshape(shape[0], shape[1])\n",
    "    return mask\n"
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   "source": [
    "## 2. Metadata\n",
    "\n",
    "MRIs are in 16-bit grayscale PNG format, not in DICOM format so their metadata is not available. However, filenames of slices include image height, width and pixel spacings on x and y axes. That information can be extracted from them.\n",
    "\n",
    "* `image_height` - Image height in pixels\n",
    "* `image_width` - Image width in pixels\n",
    "* `image_vertical_pixel_spacing` - Physical distance in real world that corresponds to vertical distance between two pixel centers\n",
    "* `image_horizontal_pixel_spacing` - Physical distance in real world that corresponds to horizontal distance between two pixel centers\n",
    "\n",
    "Image statistics such as mean, std, min and max can be extracted for normalization and further analysis.\n",
    "\n",
    "* `image_mean` - Image mean pixel value\n",
    "* `image_std` - Image standard deviation of pixel values \n",
    "* `image_min` - Image minimum pixel value\n",
    "* `image_max` - Image maximum pixel value\n",
    "\n",
    "Finally, additional statistics such as area of objects can be extracted from annotations.\n",
    "\n",
    "* `mask_area` - Total area in binary segmentation mask"
   ]
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   "source": [
    "slice_filenames = glob('../input/uw-madison-gi-tract-image-segmentation/train/*/*/scans/*.png')\n",
    "\n",
    "for filename in tqdm(slice_filenames):\n",
    "    \n",
    "    case, day = filename.split('/')[5].split('_')\n",
    "    filename_split = filename.split('/')[-1].split('_')\n",
    "    slice_number = filename_split[1]\n",
    "    image_height = int(filename_split[2])\n",
    "    image_width = int(filename_split[3])\n",
    "    image_vertical_pixel_spacing = float(filename_split[4])\n",
    "    image_horizontal_pixel_spacing = float(filename_split[5].replace('.png', ''))\n",
    "    \n",
    "    # Extract metadata from filename\n",
    "    slice_idx = (df_train['case'] == case) & (df_train['day'] == day) & (df_train['slice_number'] == slice_number)\n",
    "    df_train.loc[slice_idx, 'image_height'] = image_height\n",
    "    df_train.loc[slice_idx, 'image_width'] = image_width\n",
    "    df_train.loc[slice_idx, 'image_vertical_pixel_spacing'] = image_vertical_pixel_spacing\n",
    "    df_train.loc[slice_idx, 'image_horizontal_pixel_spacing'] = image_horizontal_pixel_spacing\n",
    "    \n",
    "    # Extract metadata from image\n",
    "    image = cv2.imread(filename, -1)\n",
    "    df_train.loc[slice_idx, 'image_mean'] = np.mean(image)\n",
    "    df_train.loc[slice_idx, 'image_std'] = np.std(image)\n",
    "    df_train.loc[slice_idx, 'image_min'] = np.min(image)\n",
    "    df_train.loc[slice_idx, 'image_max'] = np.max(image)\n",
    "    \n",
    "    # Extract metadata from annotation\n",
    "    for row_idx, row in df_train.loc[slice_idx].iterrows():\n",
    "        if isinstance(row['segmentation'], str):\n",
    "            mask = decode_rle_mask(row['segmentation'], (image_height, image_width))\n",
    "            df_train.loc[row_idx, 'mask_area'] = np.sum(mask)\n",
    "\n",
    "\n",
    "df_train['slice_count'] = df_train.groupby(['case', 'day'])['slice_number'].transform('count') / 3\n",
    "df_train['slice_count'] = df_train['slice_count'].astype(np.uint16)\n",
    "df_train['image_height'] = df_train['image_height'].astype(np.uint16)\n",
    "df_train['image_width'] = df_train['image_width'].astype(np.uint16)\n",
    "df_train['image_vertical_pixel_spacing'] = df_train['image_vertical_pixel_spacing'].astype(np.float32)\n",
    "df_train['image_horizontal_pixel_spacing'] = df_train['image_horizontal_pixel_spacing'].astype(np.float32)\n",
    "df_train['image_mean'] = df_train['image_mean'].astype(np.float32)\n",
    "df_train['image_std'] = df_train['image_std'].astype(np.float32)\n",
    "df_train['image_min'] = df_train['image_min'].astype(np.float32)\n",
    "df_train['image_max'] = df_train['image_max'].astype(np.float32)"
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   "source": [
    "There are 4 different image dimensions in training set and they are 266x266, 360x310, 276x276 and 234x234. Last two of them are very rare and most of the images are either 266x266 or 360x310. However, raw image dimensions are meaningless since there are borders around them. After removing borders, their dimensions would became smaller. Slice count is 144 in the most of the cases but some of the scans have 80 slices."
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\n",
      "text/plain": [
       "<Figure size 2400x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['image_dimensions'] = df_train['slice_count'].astype(str) + 'x' + df_train['image_height'].astype(str) + 'x' + df_train['image_width'].astype(str)\n",
    "\n",
    "def visualize_image_dimensions_distribution(df):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(24, 5), dpi=100)\n",
    "\n",
    "    sns.barplot(\n",
    "        x=df['image_dimensions'].value_counts().index,\n",
    "        y=df['image_dimensions'].value_counts().values,\n",
    "        ax=ax\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xticklabels([f'{x} ({value_count:,})' for value_count, x in zip(df['image_dimensions'].value_counts().values, df['image_dimensions'].value_counts().index)])\n",
    "    ax.tick_params(axis='x', labelsize=15, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=15, pad=10)\n",
    "    ax.set_title('Number of Unique Dimensions in Training Set', size=20, pad=15)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "visualize_image_dimensions_distribution(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0876b150",
   "metadata": {
    "papermill": {
     "duration": 0.018143,
     "end_time": "2022-04-15T16:19:24.388341",
     "exception": false,
     "start_time": "2022-04-15T16:19:24.370198",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Most of the images have 1.5 mm pixel spacings in both vertical and horizontal axes. There are only 3600 images that have 1.63 mm pixel spacings and those images are the same ones with 276x276 image dimensions. Even though metadata is not provided, evidences suggest that MRI scans in training set are most likely taken from the same machine with similar configurations. Scans with 80x276x276 dimensions could be exception."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f709e070",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-04-15T16:19:24.426966Z",
     "iopub.status.busy": "2022-04-15T16:19:24.426666Z",
     "iopub.status.idle": "2022-04-15T16:19:25.107536Z",
     "shell.execute_reply": "2022-04-15T16:19:25.106955Z"
    },
    "papermill": {
     "duration": 0.702771,
     "end_time": "2022-04-15T16:19:25.109513",
     "exception": false,
     "start_time": "2022-04-15T16:19:24.406742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['image_pixel_spacings'] = df_train['image_vertical_pixel_spacing'].astype(str) + 'x' + df_train['image_horizontal_pixel_spacing'].astype(str)\n",
    "\n",
    "def visualize_image_pixel_spacings_distribution(df):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(24, 5), dpi=100)\n",
    "\n",
    "    sns.barplot(\n",
    "        x=df['image_pixel_spacings'].value_counts().index,\n",
    "        y=df['image_pixel_spacings'].value_counts().values,\n",
    "        ax=ax\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xticklabels([f'{x} ({value_count:,})' for value_count, x in zip(df['image_pixel_spacings'].value_counts().values, df['image_pixel_spacings'].value_counts().index)])\n",
    "    ax.tick_params(axis='x', labelsize=15, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=15, pad=10)\n",
    "    ax.set_title('Number of Unique Image Pixel Spacings in Training Set', size=20, pad=15)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "visualize_image_pixel_spacings_distribution(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875384ba",
   "metadata": {
    "papermill": {
     "duration": 0.019816,
     "end_time": "2022-04-15T16:19:25.149372",
     "exception": false,
     "start_time": "2022-04-15T16:19:25.129556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Objects\n",
    "\n",
    "There are 3 classes of objects in annotations. They are large bowel, small bowel and stomach. Their number of annotations are in this order as from most to least annotated class. Differences in their number of annotations show that large bowel takes more space on z-axis while others take less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe93e8f8",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-04-15T16:19:25.191193Z",
     "iopub.status.busy": "2022-04-15T16:19:25.190868Z",
     "iopub.status.idle": "2022-04-15T16:19:25.484837Z",
     "shell.execute_reply": "2022-04-15T16:19:25.484004Z"
    },
    "papermill": {
     "duration": 0.317414,
     "end_time": "2022-04-15T16:19:25.486992",
     "exception": false,
     "start_time": "2022-04-15T16:19:25.169578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_annotation_distribution(df):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(24, 5), dpi=100)\n",
    "\n",
    "    sns.barplot(\n",
    "        x=df.loc[~df['segmentation'].isnull(), 'class'].value_counts().index,\n",
    "        y=df.loc[~df['segmentation'].isnull(), 'class'].value_counts().values,\n",
    "        ax=ax\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xticklabels([f'{x} ({value_count:,})' for value_count, x in zip(df.loc[~df['segmentation'].isnull(), 'class'].value_counts().values, df.loc[~df['segmentation'].isnull(), 'class'].value_counts().index)])\n",
    "    ax.tick_params(axis='x', labelsize=15, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=15, pad=10)\n",
    "    ax.set_title('Number of Annotations in Training Set', size=20, pad=15)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "visualize_annotation_distribution(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9caa57f0",
   "metadata": {
    "papermill": {
     "duration": 0.021339,
     "end_time": "2022-04-15T16:19:25.529947",
     "exception": false,
     "start_time": "2022-04-15T16:19:25.508608",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Mask area distributions among classes are quite different. Small bowel areas are larger than large bowel areas which suggests that small bowel takes more space in x and y axes while large bowel takes more space in z-axis. Stomach is the smallest class which can be seen from its mask area distribution and number of annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd67fb7a",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-04-15T16:19:25.574905Z",
     "iopub.status.busy": "2022-04-15T16:19:25.574621Z",
     "iopub.status.idle": "2022-04-15T16:19:26.856921Z",
     "shell.execute_reply": "2022-04-15T16:19:26.855814Z"
    },
    "papermill": {
     "duration": 1.308456,
     "end_time": "2022-04-15T16:19:26.859987",
     "exception": false,
     "start_time": "2022-04-15T16:19:25.551531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask_area\n",
      "---------\n",
      "Class large_bowel Mean: 1557.2295  -  Median: 1312.0000  -  Std: 1104.3189 - Min: 11.0000 -  Max: 6964.0000\n",
      "Class small_bowel Mean: 1792.7473  -  Median: 1600.0000  -  Std: 1168.3695 - Min: 29.0000 -  Max: 8025.0000\n",
      "Class stomach Mean: 1289.1906  -  Median: 962.0000  -  Std: 1031.8699 - Min: 12.0000 -  Max: 6917.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_feature_distribution_for_classes(df, feature):\n",
    "    \n",
    "    print(f'{feature}\\n{\"-\" * len(feature)}')\n",
    "    print(f'Class large_bowel Mean: {df.loc[df[\"class\"] == \"large_bowel\"][feature].mean():.4f}  -  Median: {df.loc[df[\"class\"] == \"large_bowel\"][feature].median():.4f}  -  Std: {df.loc[df[\"class\"] == \"large_bowel\"][feature].std():.4f} - Min: {df.loc[df[\"class\"] == \"large_bowel\"][feature].min():.4f} -  Max: {df.loc[df[\"class\"] == \"large_bowel\"][feature].max():.4f}')\n",
    "    print(f'Class small_bowel Mean: {df.loc[df[\"class\"] == \"small_bowel\"][feature].mean():.4f}  -  Median: {df.loc[df[\"class\"] == \"small_bowel\"][feature].median():.4f}  -  Std: {df.loc[df[\"class\"] == \"small_bowel\"][feature].std():.4f} - Min: {df.loc[df[\"class\"] == \"small_bowel\"][feature].min():.4f} -  Max: {df.loc[df[\"class\"] == \"small_bowel\"][feature].max():.4f}')\n",
    "    print(f'Class stomach Mean: {df.loc[df[\"class\"] == \"stomach\"][feature].mean():.4f}  -  Median: {df.loc[df[\"class\"] == \"stomach\"][feature].median():.4f}  -  Std: {df.loc[df[\"class\"] == \"stomach\"][feature].std():.4f} - Min: {df.loc[df[\"class\"] == \"stomach\"][feature].min():.4f} -  Max: {df.loc[df[\"class\"] == \"stomach\"][feature].max():.4f}')\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(24, 8), dpi=100)\n",
    "    sns.kdeplot(df.loc[df['class'] == 'large_bowel'][feature], label='large_bowel', fill=True)\n",
    "    sns.kdeplot(df.loc[df['class'] == 'small_bowel'][feature], label='small_bowel', fill=True)\n",
    "    sns.kdeplot(df.loc[df['class'] == 'stomach'][feature], label='stomach', fill=True)\n",
    "    \n",
    "    ax.legend(prop={'size': 16})\n",
    "    ax.tick_params(axis='x', labelsize=12.5, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=12.5, pad=10)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_title(f'{feature} Distribution in Training Set', fontsize=20, pad=15)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "features = ['mask_area']\n",
    "for feature in features:\n",
    "    visualize_feature_distribution_for_classes(df=df_train, feature=feature)\n"
   ]
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    "## 4. MRI Scans"
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