{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Initializations","metadata":{}},{"cell_type":"markdown","source":"This notebook shows an in-depth Exploratory Data Analysis (EDA) of the **aortic HU** measure. \n\nI hope it clarifies how the aortic HU measure may help us in detecting particular organ injuries mainly the **Extravasation** or **Bowel**.\n\n![image.png](attachment:4a32690e-1e05-4b27-ba80-c4b0dce0c9ef.png)","metadata":{},"attachments":{"4a32690e-1e05-4b27-ba80-c4b0dce0c9ef.png":{"image/png":"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"}}},{"cell_type":"code","source":"from glob import glob\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport random\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nsns.set_style(\"white\")\n\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nimport os\nimport copy\n\nfrom PIL import Image as im\nimport cv2\n\nfrom tqdm import tqdm\nimport gc\nimport pydicom as dcm","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:17.937369Z","iopub.execute_input":"2023-09-01T20:48:17.937898Z","iopub.status.idle":"2023-09-01T20:48:17.946685Z","shell.execute_reply.started":"2023-09-01T20:48:17.937857Z","shell.execute_reply":"2023-09-01T20:48:17.945477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA targets csv","metadata":{}},{"cell_type":"code","source":"targets = pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection/train.csv')\ntargets = targets.set_index('patient_id', drop=True)\ntargets","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:17.973187Z","iopub.execute_input":"2023-09-01T20:48:17.974364Z","iopub.status.idle":"2023-09-01T20:48:18.017528Z","shell.execute_reply.started":"2023-09-01T20:48:17.974296Z","shell.execute_reply":"2023-09-01T20:48:18.016323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"targets.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:18.019845Z","iopub.execute_input":"2023-09-01T20:48:18.020508Z","iopub.status.idle":"2023-09-01T20:48:18.030575Z","shell.execute_reply.started":"2023-09-01T20:48:18.020475Z","shell.execute_reply":"2023-09-01T20:48:18.029692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"targets.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:18.088626Z","iopub.execute_input":"2023-09-01T20:48:18.089063Z","iopub.status.idle":"2023-09-01T20:48:18.100491Z","shell.execute_reply.started":"2023-09-01T20:48:18.089028Z","shell.execute_reply":"2023-09-01T20:48:18.09941Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Find correlations more than 0.1 in magnitude","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1, figsize=(10,10))\n\ncorr = targets.corr()\ncorr = np.where(abs(corr) < 0.1, np.nan, corr)\n\nsns.heatmap( corr, cmap='seismic', annot=True, fmt='.2f', annot_kws={'fontsize': 9}, ax=ax, xticklabels=targets.columns, yticklabels=targets.columns)\nax.set_aspect(1)","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:18.124663Z","iopub.execute_input":"2023-09-01T20:48:18.12512Z","iopub.status.idle":"2023-09-01T20:48:19.18493Z","shell.execute_reply.started":"2023-09-01T20:48:18.125074Z","shell.execute_reply":"2023-09-01T20:48:19.183669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Extract column categories","metadata":{}},{"cell_type":"code","source":"organs = ['bowel', 'extravasation', 'kidney', 'liver', 'spleen']\n\nbowel_cols = ['bowel_healthy', 'bowel_injury']\nextravasation_cols = ['extravasation_healthy', 'extravasation_injury']\nkidney_cols = ['kidney_healthy', 'kidney_low', 'kidney_high']\nliver_cols = ['liver_healthy', 'liver_low', 'liver_high']\nspleen_cols = ['spleen_healthy', 'spleen_low', 'spleen_high']\nany_injury_cols = ['any_injury']","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:19.187687Z","iopub.execute_input":"2023-09-01T20:48:19.188076Z","iopub.status.idle":"2023-09-01T20:48:19.194855Z","shell.execute_reply.started":"2023-09-01T20:48:19.188041Z","shell.execute_reply":"2023-09-01T20:48:19.193659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Plot pie chart per organ","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(2, 3, figsize=(20, 10))\nax = ax.ravel()\n\nfor counter, organ in enumerate(organs):\n    cols = eval(organ + '_cols')\n    temp = targets[cols].copy()    \n    plt.sca(ax[counter])\n    plt.pie(temp.sum(), labels=[col.replace(organ + '_', '') for col in cols], radius=0.75, autopct='%1.0f%%', pctdistance=0.7)\n    plt.title(organ.upper())\n    \ntemp = targets[any_injury_cols].copy()    \ntemp['no_injury'] = 1 - temp[any_injury_cols]\nplt.sca(ax[-1])\n_ = plt.pie(temp.sum(), labels=['present','absent'], autopct='%1.0f%%', pctdistance=0.7)\n_ = plt.title('any_injury'.upper())","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:19.196687Z","iopub.execute_input":"2023-09-01T20:48:19.19725Z","iopub.status.idle":"2023-09-01T20:48:20.04317Z","shell.execute_reply.started":"2023-09-01T20:48:19.197218Z","shell.execute_reply":"2023-09-01T20:48:20.041914Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Can we have both low and high injury in one patient??","metadata":{}},{"cell_type":"code","source":"for organ in organs[2:]:\n    cols = eval(organ + '_cols')\n    temp = targets[cols[1:]]\n    print(f'{organ} rows with both low and high injury markers: {temp.min(axis=1).sum()}')","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:20.0462Z","iopub.execute_input":"2023-09-01T20:48:20.046633Z","iopub.status.idle":"2023-09-01T20:48:20.060377Z","shell.execute_reply.started":"2023-09-01T20:48:20.046598Z","shell.execute_reply":"2023-09-01T20:48:20.058954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA on Aortic HU data","metadata":{}},{"cell_type":"code","source":"sessions = pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection/train_series_meta.csv')\nsessions = sessions.sort_values(by=['patient_id','series_id'])\nsessions = sessions.reset_index(drop=True)\nsessions","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:20.061807Z","iopub.execute_input":"2023-09-01T20:48:20.062581Z","iopub.status.idle":"2023-09-01T20:48:20.088731Z","shell.execute_reply.started":"2023-09-01T20:48:20.062549Z","shell.execute_reply":"2023-09-01T20:48:20.087679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Find number of sessions per patient","metadata":{}},{"cell_type":"code","source":"# find number of sessions per subject\ntemp = sessions.groupby('patient_id').nunique()['series_id']\nsessions['num_sessions'] = sessions.apply(lambda row: temp[ row['patient_id'] ], axis=1)\nsessions","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:20.090166Z","iopub.execute_input":"2023-09-01T20:48:20.09053Z","iopub.status.idle":"2023-09-01T20:48:20.223188Z","shell.execute_reply.started":"2023-09-01T20:48:20.090499Z","shell.execute_reply":"2023-09-01T20:48:20.222082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = sessions.groupby('patient_id').max()['num_sessions']\ntemp = pd.get_dummies(temp, prefix='num_sessions_')\n_ = plt.pie( temp.sum(), labels=temp.columns, pctdistance=0.5, autopct='%.2f%%')\n_ = plt.title('Number of sessions across subjects'.upper())","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:20.225116Z","iopub.execute_input":"2023-09-01T20:48:20.225604Z","iopub.status.idle":"2023-09-01T20:48:20.404725Z","shell.execute_reply.started":"2023-09-01T20:48:20.225552Z","shell.execute_reply":"2023-09-01T20:48:20.403119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# What is the distribution of the aortic HU","metadata":{}},{"cell_type":"markdown","source":"### Across all sessions","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1,figsize=(15, 3))\n_ = sns.histplot(sessions['aortic_hu'], kde=True, linewidth=0.1, ax=ax, alpha=0.3)","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:20.40997Z","iopub.execute_input":"2023-09-01T20:48:20.411495Z","iopub.status.idle":"2023-09-01T20:48:21.142882Z","shell.execute_reply.started":"2023-09-01T20:48:20.411419Z","shell.execute_reply":"2023-09-01T20:48:21.141712Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Across two sessions among subjects who have two sessions","metadata":{}},{"cell_type":"code","source":"two_session_df = sessions.loc[sessions['num_sessions'] == 2]\nlow_HUs = two_session_df.groupby('patient_id').idxmin()['aortic_hu']\nhigh_HUs = two_session_df.groupby('patient_id').idxmax()['aortic_hu']\n\nlow_HU_df = two_session_df.loc[two_session_df['aortic_hu'].index.isin( low_HUs ), :]\nhigh_HU_df = two_session_df.loc[two_session_df['aortic_hu'].index.isin( high_HUs ), :]\n\nsessions['HU_kind'] = sessions.apply(lambda row: 'High' if row.name in high_HUs.values else 'Low' if row.name in low_HUs.values else 'Single', axis=1)\nsessions","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:21.144667Z","iopub.execute_input":"2023-09-01T20:48:21.145891Z","iopub.status.idle":"2023-09-01T20:48:23.403938Z","shell.execute_reply.started":"2023-09-01T20:48:21.145844Z","shell.execute_reply":"2023-09-01T20:48:23.402752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure( figsize=(20, 8) )\ngs = fig.add_gridspec(nrows=2, ncols=3, hspace=0.3, wspace=0.3)\n\n# ----------- Hist plots\nax = fig.add_subplot(gs[0,:2])\nplt.sca( ax )\n_ = sns.histplot(low_HU_df['aortic_hu'], kde=True, linewidth=0.1, alpha=0.3, binwidth=10, label='Low_HUs')\n_ = sns.histplot(high_HU_df['aortic_hu'], kde=True, linewidth=0.1, alpha=0.3, binwidth=10, label='High_HUs')\n_ = plt.legend()\n_ = plt.title( 'Distribution of high HUs and low HUs'.upper() )\n\nax = fig.add_subplot(gs[1,:2])\nplt.sca( ax )\n_ = sns.histplot(low_HU_df['aortic_hu'], kde=True, linewidth=0.1, alpha=0.3, binwidth=10, label='Low_HUs')\n_ = sns.histplot(high_HU_df['aortic_hu'], kde=True, linewidth=0.1, alpha=0.3, binwidth=10, label='High_HUs')\nplt.xlim([0, 800])\n_ = plt.legend()\n_ = plt.title( 'Zoomed version of \"Distribution of high HUs and low HUs\"'.upper() )\n\n\n# ----------- scatter plots\nax = fig.add_subplot(gs[0, 2])\nplt.sca(ax )\nplt.scatter(low_HU_df['aortic_hu'].values, high_HU_df['aortic_hu'].values, s=1, alpha=0.5)\nplt.xlabel('Low_HU')\nplt.xlabel('High_HU')\n_ = plt.title( 'Pairings of high HUs and low HUs'.upper() )\n\nax = fig.add_subplot(gs[1, 2])\nplt.sca(ax )\nplt.scatter(low_HU_df['aortic_hu'].values, high_HU_df['aortic_hu'].values, s=6, alpha=0.5)\nplt.xlim([0, 450])\nplt.ylim([0, 900])\nplt.xlabel('Low_HU')\nplt.xlabel('High_HU')\n_ = plt.title( 'Zoomed version of \"Pairings of high HUs and low HUs\"'.upper() )","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:23.408903Z","iopub.execute_input":"2023-09-01T20:48:23.409293Z","iopub.status.idle":"2023-09-01T20:48:26.878523Z","shell.execute_reply.started":"2023-09-01T20:48:23.40926Z","shell.execute_reply":"2023-09-01T20:48:26.877161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### How many sessions have incomplete organs in them","metadata":{}},{"cell_type":"code","source":"sns.countplot(x=sessions['incomplete_organ'], hue=sessions['HU_kind'], hue_order=['Single', 'Low', 'High'], palette=sns.color_palette('coolwarm', n_colors=3))\nplt.title('HOW MANY incomplete scans in each session Vs. HU'.upper())","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:26.880078Z","iopub.execute_input":"2023-09-01T20:48:26.880549Z","iopub.status.idle":"2023-09-01T20:48:27.29451Z","shell.execute_reply.started":"2023-09-01T20:48:26.880515Z","shell.execute_reply":"2023-09-01T20:48:27.293367Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Which HU (low or high) shows bowel or extravasation injury better??","metadata":{}},{"cell_type":"code","source":"instances = pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection/image_level_labels.csv')\ninstances","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:27.295898Z","iopub.execute_input":"2023-09-01T20:48:27.296244Z","iopub.status.idle":"2023-09-01T20:48:27.322775Z","shell.execute_reply.started":"2023-09-01T20:48:27.296213Z","shell.execute_reply":"2023-09-01T20:48:27.321597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = instances.groupby(['patient_id', 'series_id'])['injury_name'].value_counts().unstack().fillna(0).astype(int)\ntemp = temp.applymap(lambda x: 'Present' if x>0 else 'Absent')\n\nsns.countplot(x=temp['Active_Extravasation'], order=['Absent', 'Present'], hue=temp['Bowel'], hue_order=['Absent', 'Present'], palette=sns.color_palette('seismic', n_colors=2))\n_ = plt.title('Bowel vs Extravasation'.upper())","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:27.324278Z","iopub.execute_input":"2023-09-01T20:48:27.325479Z","iopub.status.idle":"2023-09-01T20:48:27.67955Z","shell.execute_reply.started":"2023-09-01T20:48:27.325446Z","shell.execute_reply":"2023-09-01T20:48:27.678355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### merge instances csv file onto HU csv file","metadata":{}},{"cell_type":"code","source":"temp = instances.groupby(['patient_id', 'series_id'])['injury_name'].value_counts().unstack().fillna(0).astype(int)\n\nHU_injury = sessions.merge(temp, how='left', on=['patient_id', 'series_id'])\nHU_injury.fillna(np.nan, inplace=True)\nHU_injury","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:27.680825Z","iopub.execute_input":"2023-09-01T20:48:27.681145Z","iopub.status.idle":"2023-09-01T20:48:27.718634Z","shell.execute_reply.started":"2023-09-01T20:48:27.681115Z","shell.execute_reply":"2023-09-01T20:48:27.717441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"HU_injury = HU_injury.merge(targets, how='left', on=['patient_id'])\nHU_injury","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:27.721859Z","iopub.execute_input":"2023-09-01T20:48:27.722598Z","iopub.status.idle":"2023-09-01T20:48:27.757717Z","shell.execute_reply.started":"2023-09-01T20:48:27.722561Z","shell.execute_reply":"2023-09-01T20:48:27.756288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Look at patients with Extravsation injury","metadata":{}},{"cell_type":"code","source":"df = HU_injury.loc[HU_injury['extravasation_injury']==1, :]\n\nsns.histplot( df.loc[df['HU_kind'] == 'Low', 'Active_Extravasation'], binwidth=5, label='Low_HU', color='b', kde=True)\nsns.histplot( df.loc[df['HU_kind'] == 'High', 'Active_Extravasation'], binwidth=5, label='High_HU', color='r', kde=True)\n_ = plt.legend()\n_ = plt.title( 'EXtravasation detection across HU settings'.upper() )","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:27.759133Z","iopub.execute_input":"2023-09-01T20:48:27.760183Z","iopub.status.idle":"2023-09-01T20:48:28.484563Z","shell.execute_reply.started":"2023-09-01T20:48:27.760136Z","shell.execute_reply":"2023-09-01T20:48:28.483419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Look at patients with Bowel injury","metadata":{}},{"cell_type":"code","source":"df = HU_injury.loc[HU_injury['bowel_injury']==1, :]\n\nsns.histplot( df.loc[df['HU_kind'] == 'Low', 'Bowel'], binwidth=5, label='Low_HU', color='b', kde=True)\nsns.histplot( df.loc[df['HU_kind'] == 'High', 'Bowel'], binwidth=5, label='High_HU', color='r', kde=True)\n_ = plt.legend()\n_ = plt.title( 'Bowel injury detection across HU settings'.upper() )","metadata":{"execution":{"iopub.status.busy":"2023-09-01T20:48:28.486109Z","iopub.execute_input":"2023-09-01T20:48:28.487129Z","iopub.status.idle":"2023-09-01T20:48:29.247485Z","shell.execute_reply.started":"2023-09-01T20:48:28.487089Z","shell.execute_reply":"2023-09-01T20:48:29.24643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The Data does not show a considerable difference in the prevalence of detected bowel or extravasation injury across the two HU settings. Competition hosts have mentioned that Higher HU is better for classification of extravasation while lower HU is better for solid organ classification. Although I did not find this pattern in the data, however, the data may be biased due to the fact that different number of slices exist (or are reported) per session. Therefore, direct comparison of detected injury across HU may not be bias-free.**","metadata":{}}],"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}}