{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":99552,"databundleVersionId":13694723,"sourceType":"competition"}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Import libraries\nimport os\nimport glob\nimport numpy as np\nimport pandas as pd\nimport pydicom as pdc\nimport matplotlib.pyplot as plt\n\ndir_path    = \"/kaggle/input/rsna-intracranial-aneurysm-detection\"\nseries_path =  dir_path + \"/series\"\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-09-19T21:39:38.669125Z","iopub.execute_input":"2025-09-19T21:39:38.669499Z","iopub.status.idle":"2025-09-19T21:39:42.383629Z","shell.execute_reply.started":"2025-09-19T21:39:38.669466Z","shell.execute_reply":"2025-09-19T21:39:42.382547Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pprint as pp\nimport os\n\n# Read CSVs\ndata_df = pd.read_csv(f\"{dir_path}/train.csv\")\ndata_loc_df = pd.read_csv(f\"{dir_path}/train_localizers.csv\")\n\n# Print summary stats\npp.pprint({\n    \"Total Series Data\"      : data_df.shape[0],\n    \"Total Aneurysm Present\" : data_df[data_df[\"Aneurysm Present\"] == 1].shape[0],\n    \"Total DICOM loc samples\": data_loc_df.shape[0],\n    \"Unique DICOM in series\" : data_loc_df[\"SeriesInstanceUID\"].nunique()\n})\n\n# Remove rows where aneurysm is present but no local sample exists\nmask = (data_df[\"Aneurysm Present\"] == 1) & (~data_df[\"SeriesInstanceUID\"].isin(data_loc_df[\"SeriesInstanceUID\"]))\ndata_df = data_df.loc[~mask].copy()\n\n# Build SOPInstanceUID list for each SeriesInstanceUID\ndicom_df = data_df[\"SeriesInstanceUID\"].apply(\n    lambda uid: [\n        {\"SOPInstanceUID\": fname, \"SeriesInstanceUID\": uid}\n        for fname in os.listdir(f\"{series_path}/{uid}\")\n    ]\n)\n\ndicom_df = pd.DataFrame([item for sublist in dicom_df for item in sublist])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-19T23:09:07.375519Z","iopub.execute_input":"2025-09-19T23:09:07.37587Z","iopub.status.idle":"2025-09-19T23:09:11.083336Z","shell.execute_reply.started":"2025-09-19T23:09:07.375845Z","shell.execute_reply":"2025-09-19T23:09:11.082511Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# data_df[[\"SeriesInstanceUID\", \"PatientAge\", \"Patient Sex\", \"Modality\"]]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-19T23:09:55.179551Z","iopub.execute_input":"2025-09-19T23:09:55.179857Z","iopub.status.idle":"2025-09-19T23:09:55.186423Z","shell.execute_reply.started":"2025-09-19T23:09:55.179836Z","shell.execute_reply":"2025-09-19T23:09:55.18565Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"merged_df_1 = pd.merge(\n    dicom_df,\n    data_df[['SeriesInstanceUID', 'PatientAge', 'PatientSex', 'Modality']],\n    on=\"SeriesInstanceUID\",\n    how=\"right\"\n)\n\nmerged_df_1\n\n# merged_df_2 = pd.merge(\n#     merged_df_1,\n#     data_loc_df,\n#     on=[\"SeriesInstanceUID\", \"SOPInstanceUID\"],\n#     how=\"right\"\n# )\n\n# merged_df_2","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-19T23:12:06.539708Z","iopub.execute_input":"2025-09-19T23:12:06.540063Z","iopub.status.idle":"2025-09-19T23:12:06.787032Z","shell.execute_reply.started":"2025-09-19T23:12:06.54004Z","shell.execute_reply":"2025-09-19T23:12:06.785942Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df[train_df[\"Modality\"] == \"CTA\"][\"DICOM count\"].sum()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_loc_df[\"location\"].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-19T21:59:11.079868Z","iopub.execute_input":"2025-09-19T21:59:11.080519Z","iopub.status.idle":"2025-09-19T21:59:11.088264Z","shell.execute_reply.started":"2025-09-19T21:59:11.080491Z","shell.execute_reply":"2025-09-19T21:59:11.087393Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample_series = sample_df[\"SeriesInstanceUID\"]\n\nseries_files = glob.glob(f\"{series_path}/{sample_series}/*\")\n\ndcm_list = [f.split(\"/\")[-1][:-4] for f in series_files]\n\n\nif sample_uid in dcm_list:\n    index = dcm_list.index(sample_uid)\n    print(f\"Index of Sample: {index}/{len(series_files)}\")\n\n    indices = [23, 46, 68, 91, 114, 137, 157, 182, 204]\n\n    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n    axes = axes.flatten()  # flatten to 1D for easy iteration\n\n    for ax, i in zip(axes, indices):\n        img = pdc.dcmread(series_files[i]).pixel_array\n        ax.imshow(img, cmap=\"gray\")\n        ax.set_title(f\"Slice {i}\")\n        ax.axis(\"off\")\n\n    plt.tight_layout()\n    plt.show()\nelse:\n    print(f\"{sample_uid} not found in series.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-13T17:33:05.254624Z","iopub.execute_input":"2025-09-13T17:33:05.254924Z","iopub.status.idle":"2025-09-13T17:33:06.545059Z","shell.execute_reply.started":"2025-09-13T17:33:05.2549Z","shell.execute_reply":"2025-09-13T17:33:06.543838Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"series_files = glob.glob(f\"{series_path}/{sample_series}/*\")\n\n\ndcm_list = [f.split(\"/\")[-1][:-4] for f in series_files]\n\n\nif sample_uid in dcm_list:\n    index = dcm_list.index(sample_uid)\n    print(f\"Index of Sample: {index}/{len(series_files)}\")\n\n    indices = list(range(157-5, 157+4))\n\n    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n    axes = axes.flatten()  # flatten to 1D for easy iteration\n\n    for ax, i in zip(axes, indices):\n        img = pdc.dcmread(series_files[i]).pixel_array\n        ax.imshow(img, cmap=\"gray\")\n        ax.set_title(f\"Slice {i}\")\n        ax.axis(\"off\")\n\n    plt.tight_layout()\n    plt.show()\n    \nelse:\n    print(f\"{sample_uid} not found in series.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-13T16:04:59.990545Z","iopub.execute_input":"2025-09-13T16:04:59.990841Z","iopub.status.idle":"2025-09-13T16:05:01.401359Z","shell.execute_reply.started":"2025-09-13T16:04:59.99082Z","shell.execute_reply":"2025-09-13T16:05:01.400204Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:d04c9ae4-0561-4fc5-89a7-b1539495f8e0.png)","metadata":{},"attachments":{"d04c9ae4-0561-4fc5-89a7-b1539495f8e0.png":{"image/png":"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"}}},{"cell_type":"code","source":"import pydicom\n\n# Load the DICOM file\ndicom_path = series_files[157]\nds = pydicom.dcmread(dicom_path)\n\n# Print all DICOM tags with their values\nprint(\"===== DICOM Metadata =====\")\nfor elem in ds:\n    if elem.VR != \"SQ\" and elem.name != \"Pixel Data\":  # SQ = Sequence, handled separately\n        print(f\"{elem.tag} : {elem.name} = {elem.value}\")\n    else:\n        print(f\"{elem.tag} : {elem.name} = (Sequence with {len(elem.value)} items)\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-13T16:47:22.31502Z","iopub.execute_input":"2025-09-13T16:47:22.315346Z","iopub.status.idle":"2025-09-13T16:47:22.327958Z","shell.execute_reply.started":"2025-09-13T16:47:22.315325Z","shell.execute_reply":"2025-09-13T16:47:22.326812Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}