{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"},{"sourceId":9471315,"sourceType":"datasetVersion","datasetId":5587468}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install seaborn==0.13.0","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:15.388156Z","iopub.execute_input":"2024-09-25T12:23:15.388923Z","iopub.status.idle":"2024-09-25T12:23:29.531762Z","shell.execute_reply.started":"2024-09-25T12:23:15.388886Z","shell.execute_reply":"2024-09-25T12:23:29.530564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom math import acos,asin\n\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\nimport os\nimport gc\n\nimport pydicom as dicom\nfrom pydicom.tag import BaseTag\n\nfrom torch.utils.data import Dataset\n\nfrom IPython.display import Latex, HTML, Math\nfrom tqdm import tqdm\nimport warnings\nimport seaborn as sns\nimport shutil\nwarnings.filterwarnings('ignore')\nplt.style.use(\"default\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-25T13:11:05.591686Z","iopub.execute_input":"2024-09-25T13:11:05.592447Z","iopub.status.idle":"2024-09-25T13:11:05.600455Z","shell.execute_reply.started":"2024-09-25T13:11:05.592414Z","shell.execute_reply":"2024-09-25T13:11:05.599144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Table data loading","metadata":{}},{"cell_type":"code","source":"# Define file paths\nROOT_PATH = \"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/\"\nTRAIN_DF_PATH = os.path.join(ROOT_PATH, \"train.csv\")\nTRAIN_LABEL_COORDS_PATH = os.path.join(ROOT_PATH, \"train_label_coordinates.csv\")\nTRAIN_SERIES_PATH = os.path.join(ROOT_PATH, \"train_series_descriptions.csv\")\nTRAIN_IMG_PATH = os.path.join(ROOT_PATH, \"train_images\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.574790Z","iopub.execute_input":"2024-09-25T12:23:33.575975Z","iopub.status.idle":"2024-09-25T12:23:33.582478Z","shell.execute_reply.started":"2024-09-25T12:23:33.575931Z","shell.execute_reply":"2024-09-25T12:23:33.581017Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv(TRAIN_DF_PATH)\ntrain_label_coords_df = pd.read_csv(TRAIN_LABEL_COORDS_PATH)\ntrain_series_df = pd.read_csv(TRAIN_SERIES_PATH)","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.585377Z","iopub.execute_input":"2024-09-25T12:23:33.586533Z","iopub.status.idle":"2024-09-25T12:23:33.750410Z","shell.execute_reply.started":"2024-09-25T12:23:33.586489Z","shell.execute_reply":"2024-09-25T12:23:33.749150Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# First view","metadata":{}},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.751924Z","iopub.execute_input":"2024-09-25T12:23:33.752716Z","iopub.status.idle":"2024-09-25T12:23:33.787152Z","shell.execute_reply.started":"2024-09-25T12:23:33.752677Z","shell.execute_reply":"2024-09-25T12:23:33.786111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_label_coords_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.788967Z","iopub.execute_input":"2024-09-25T12:23:33.789407Z","iopub.status.idle":"2024-09-25T12:23:33.807897Z","shell.execute_reply.started":"2024-09-25T12:23:33.789369Z","shell.execute_reply":"2024-09-25T12:23:33.806356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_series_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.809393Z","iopub.execute_input":"2024-09-25T12:23:33.810009Z","iopub.status.idle":"2024-09-25T12:23:33.823298Z","shell.execute_reply.started":"2024-09-25T12:23:33.809969Z","shell.execute_reply":"2024-09-25T12:23:33.822134Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with pd.option_context(\"display.max_columns\",26):\n    display(train_df)","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.825348Z","iopub.execute_input":"2024-09-25T12:23:33.826207Z","iopub.status.idle":"2024-09-25T12:23:33.863431Z","shell.execute_reply.started":"2024-09-25T12:23:33.826168Z","shell.execute_reply":"2024-09-25T12:23:33.862276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with pd.option_context(\"display.max_columns\",26):\n    display(train_label_coords_df.sort_values(['study_id','series_id']).head(30))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.865039Z","iopub.execute_input":"2024-09-25T12:23:33.866166Z","iopub.status.idle":"2024-09-25T12:23:33.894764Z","shell.execute_reply.started":"2024-09-25T12:23:33.866121Z","shell.execute_reply":"2024-09-25T12:23:33.893562Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_series_df","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.899510Z","iopub.execute_input":"2024-09-25T12:23:33.899866Z","iopub.status.idle":"2024-09-25T12:23:33.914437Z","shell.execute_reply.started":"2024-09-25T12:23:33.899837Z","shell.execute_reply":"2024-09-25T12:23:33.913363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds1 = dicom.dcmread(os.path.join(TRAIN_IMG_PATH,\n                                 str(train_label_coords_df[\"study_id\"].iloc[0]),\n                                 str(train_label_coords_df[\"series_id\"].iloc[0]),\n                                 f'{train_label_coords_df[\"instance_number\"].iloc[0]}.dcm'\n                               ))\n\nstudy_id = str(train_label_coords_df[\"study_id\"].iloc[0])\nseries_id = str(train_label_coords_df[\"series_id\"]\\\n                                     [train_label_coords_df[\"study_id\"]==int(study_id)].drop_duplicates().iloc[1])\nfile_name = f'''{train_label_coords_df[\"instance_number\"]\n[train_label_coords_df[\"series_id\"]==int(series_id)].drop_duplicates().iloc[2]}.dcm'''\n                               \nds2 = dicom.dcmread(os.path.join(TRAIN_IMG_PATH,\n                                 study_id,\n                                 series_id,\n                                 file_name))\n                               \nstudy_id = str(train_label_coords_df[\"study_id\"].iloc[0])\nseries_id = str(train_label_coords_df[\"series_id\"]\\\n                                     [train_label_coords_df[\"study_id\"]==int(study_id)].drop_duplicates().iloc[2])\nfile_name = f'''{train_label_coords_df[\"instance_number\"]\n[train_label_coords_df[\"series_id\"]==int(series_id)].drop_duplicates().iloc[2]}.dcm'''\n                               \nds3 = dicom.dcmread(os.path.join(TRAIN_IMG_PATH,\n                                 study_id,\n                                 series_id,\n                                 file_name))\n                                 ","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.916092Z","iopub.execute_input":"2024-09-25T12:23:33.916466Z","iopub.status.idle":"2024-09-25T12:23:33.965286Z","shell.execute_reply.started":"2024-09-25T12:23:33.916437Z","shell.execute_reply":"2024-09-25T12:23:33.964117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(15,7))\nplt.subplot(1,3,1)\nplt.imshow(ds1.pixel_array, cmap='gray')\nplt.subplot(1,3,2)\nplt.imshow(ds2.pixel_array, cmap='gray')\nplt.subplot(1,3,3)\nplt.imshow(ds3.pixel_array, cmap='gray')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:33.966901Z","iopub.execute_input":"2024-09-25T12:23:33.967648Z","iopub.status.idle":"2024-09-25T12:23:34.766660Z","shell.execute_reply.started":"2024-09-25T12:23:33.967610Z","shell.execute_reply":"2024-09-25T12:23:34.765445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds1","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:34.768273Z","iopub.execute_input":"2024-09-25T12:23:34.769004Z","iopub.status.idle":"2024-09-25T12:23:34.779268Z","shell.execute_reply.started":"2024-09-25T12:23:34.768963Z","shell.execute_reply":"2024-09-25T12:23:34.778042Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data analysis","metadata":{}},{"cell_type":"markdown","source":"## Study/series relation","metadata":{}},{"cell_type":"markdown","source":"First, we study the structure of the data and how different IDs correspond to each other, forming our compound feature space.","metadata":{}},{"cell_type":"markdown","source":"Let's check for 'study_id' uniqueness.","metadata":{}},{"cell_type":"code","source":"len(train_df['study_id'].unique())/len(train_df['study_id'])","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:34.780799Z","iopub.execute_input":"2024-09-25T12:23:34.781499Z","iopub.status.idle":"2024-09-25T12:23:34.803201Z","shell.execute_reply.started":"2024-09-25T12:23:34.781458Z","shell.execute_reply":"2024-09-25T12:23:34.802013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Yes it is.","metadata":{"execution":{"iopub.status.busy":"2024-08-21T14:33:31.198965Z","iopub.execute_input":"2024-08-21T14:33:31.199381Z","iopub.status.idle":"2024-08-21T14:33:31.207167Z","shell.execute_reply.started":"2024-08-21T14:33:31.199349Z","shell.execute_reply":"2024-08-21T14:33:31.205496Z"}}},{"cell_type":"markdown","source":"Check if the sets of 'studies_id' are the same in train_df and train_series_df.","metadata":{}},{"cell_type":"code","source":"len(set(train_df['study_id']) & set(train_series_df['study_id']))/len(train_df['study_id'])","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:34.804655Z","iopub.execute_input":"2024-09-25T12:23:34.805000Z","iopub.status.idle":"2024-09-25T12:23:34.816612Z","shell.execute_reply.started":"2024-09-25T12:23:34.804974Z","shell.execute_reply":"2024-09-25T12:23:34.815604Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Yes it is.","metadata":{}},{"cell_type":"code","source":"train_series_df.groupby(\"study_id\")['series_id'].count().hist(edgecolor=\"black\",linewidth=0.7, linestyle=\"--\")\nplt.title(\"Number of series for study\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:34.818234Z","iopub.execute_input":"2024-09-25T12:23:34.818608Z","iopub.status.idle":"2024-09-25T12:23:35.128692Z","shell.execute_reply.started":"2024-09-25T12:23:34.818577Z","shell.execute_reply":"2024-09-25T12:23:35.127615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Image number per study/series","metadata":{}},{"cell_type":"code","source":"def series_images_number(x):\n    return len(os.listdir(os.path.join(TRAIN_IMG_PATH, str(x.name[0]), str(x.name[1]))))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:35.129980Z","iopub.execute_input":"2024-09-25T12:23:35.130316Z","iopub.status.idle":"2024-09-25T12:23:35.135422Z","shell.execute_reply.started":"2024-09-25T12:23:35.130289Z","shell.execute_reply":"2024-09-25T12:23:35.134303Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's take a look at the distribution of the number of images per series.","metadata":{}},{"cell_type":"code","source":"ser_img_number = train_series_df.groupby([\"study_id\", \"series_id\"]).apply(series_images_number)\ndisplay(Math(fr\"\"\"\\begin{{array}}{{|lc|}}\\hline Image~number~per~series \\\\\\hline \nminimum: {ser_img_number.min()} & maximum: {ser_img_number.max()}\\\\\\hline \\end{{array}}\"\"\"))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:23:35.136631Z","iopub.execute_input":"2024-09-25T12:23:35.136917Z","iopub.status.idle":"2024-09-25T12:24:16.112865Z","shell.execute_reply.started":"2024-09-25T12:23:35.136892Z","shell.execute_reply":"2024-09-25T12:24:16.111763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nax = fig.add_subplot(1,2,1)\nser_img_number.hist(bins=200,range = (0,200), edgecolor=\"black\",linewidth=0.7, linestyle=\"--\")\nax.set_title(\"Number of series with certain image number, 1 image discretization\")\nax = fig.add_subplot(1,2,2)\nser_img_number.hist(bins=40,range = (0,200), edgecolor=\"black\",linewidth=0.7, linestyle=\"--\")\nax.set_title(\"image number per series, 5 image discretization\")\n\nser_img_number_proj = train_label_coords_df.merge(train_series_df, on=[\"study_id\", \"series_id\"])\\\n.groupby([\"study_id\", \"series_id\", \"series_description\"]).apply(series_images_number).reset_index(level=2)\n\nfig, axes = plt.subplots(1,3,figsize=(15,5))\nfig.suptitle(\"Number of series with certain image number\\nbroken down by projection type\\n\",\n            fontsize=16)\n\nj=0\nfor ax, projection, color in zip(axes, ['Axial T2', 'Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n    data = ser_img_number_proj.query(\"series_description == @projection\")[0]\n    ax.set_title(projection)\n    data.hist(bins=data.max() - data.min(), color=color,ax=ax),\n    j+=1\n    ax.set_ylabel(\"Number of series\")\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:16.114210Z","iopub.execute_input":"2024-09-25T12:24:16.114520Z","iopub.status.idle":"2024-09-25T12:24:21.832035Z","shell.execute_reply.started":"2024-09-25T12:24:16.114495Z","shell.execute_reply":"2024-09-25T12:24:21.831022Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It seems that there is a bimodal distribution, with the peaks near 16-19 and a second, less pronounced peak, near 40-50.","metadata":{}},{"cell_type":"markdown","source":"Let's look at the distribution of the number of images per study.","metadata":{}},{"cell_type":"code","source":"study_img_number = ser_img_number.groupby(level=0, axis=0).sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:21.833451Z","iopub.execute_input":"2024-09-25T12:24:21.833850Z","iopub.status.idle":"2024-09-25T12:24:21.841149Z","shell.execute_reply.started":"2024-09-25T12:24:21.833814Z","shell.execute_reply":"2024-09-25T12:24:21.839975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(Math(fr\"\"\"\\begin{{array}}{{|lc|}}\\hline Image~number~per~study \\\\\\hline \nminimum: {study_img_number.min()} & maximum: {study_img_number.max()}\\\\\\hline \\end{{array}}\"\"\"))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:21.842411Z","iopub.execute_input":"2024-09-25T12:24:21.842723Z","iopub.status.idle":"2024-09-25T12:24:21.860511Z","shell.execute_reply.started":"2024-09-25T12:24:21.842697Z","shell.execute_reply":"2024-09-25T12:24:21.859438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nax = fig.add_subplot(1,2,1)\nstudy_img_number.hist(bins=study_img_number.max() - study_img_number.min() + 1,\n                      range=(study_img_number.min(), study_img_number.max() + 1),\n                      edgecolor=\"black\",linewidth=0.7, linestyle=\"--\"\n                     )\nax.set_title(\"image number per study, 1 image discretization\")\n\nax = fig.add_subplot(1,2,2)\nstudy_img_number.hist(bins=45,\n                      range=(25,250),\n                      edgecolor=\"black\",linewidth=0.7, linestyle=\"--\")\nax.set_title(\"image number per study, 5 image discretization\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:21.861926Z","iopub.execute_input":"2024-09-25T12:24:21.862297Z","iopub.status.idle":"2024-09-25T12:24:22.645340Z","shell.execute_reply.started":"2024-09-25T12:24:21.862267Z","shell.execute_reply":"2024-09-25T12:24:22.644264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Dicom files parameters","metadata":{}},{"cell_type":"markdown","source":"Now, let's take a look at some of the parameters in DICOM files that contain metadata about the image series (slice thickness, spacing between slices, image orientation relative to the patient, etc.).","metadata":{}},{"cell_type":"code","source":"def read_dicom_meta(path=TRAIN_IMG_PATH):\n    dicom_meta_df = pd.DataFrame()\n    for study in tqdm(os.listdir(path)):\n        series_dict = {\"study_id\": [],\n                       \"series_id\": [],\n                       \"series_description\": [],\n                       \"patient_position\": [], \n                       \"image_position\": [],\n                       \"image_orientation\": [],\n                       \"slice_spacing\": [],\n                       \"slice_thickness\": [],\n                       \"rows\": [],\n                       \"columns\": [],\n                       \"photometric_interpretation\": [],\n                       \"pixel_spacing\": [],\n                       \"instance_number\": []\n                    }\n        for series in os.listdir(os.path.join(path, study)):\n            series_dict[\"study_id\"].extend([int(study) for i in \n                                  range(len(os.listdir(os.path.join(path, study, series))))])\n            series_dict[\"series_id\"].extend([int(series) for i in \n                                  range(len(os.listdir(os.path.join(path, study, series))))])\n            for d_file in os.listdir(os.path.join(path, study, series)):\n                ds = dicom.dcmread(os.path.join(path, study, series, d_file),\n                                   stop_before_pixels = True)\n\n                series_dict[\"series_description\"].append(ds.SeriesDescription)\n                series_dict[\"patient_position\"].append(ds.PatientPosition)\n                series_dict[\"image_position\"].append(ds.ImagePositionPatient)\n                series_dict[\"image_orientation\"].append(ds.ImageOrientationPatient)\n                series_dict[\"slice_spacing\"].append(ds.SpacingBetweenSlices)\n                series_dict[\"slice_thickness\"].append(ds.SliceThickness)\n                series_dict[\"rows\"].append(ds.Rows)\n                series_dict[\"columns\"].append(ds.Columns)\n                series_dict[\"photometric_interpretation\"].append(ds.PhotometricInterpretation)\n                series_dict[\"pixel_spacing\"].append(ds.PixelSpacing)\n                series_dict[\"instance_number\"].append(d_file.split(\".\")[0])\n        dicom_meta_df = pd.concat([dicom_meta_df, pd.DataFrame(series_dict)], ignore_index=True)\n        series_dict[\"study_id\"] = []\n        series_dict[\"series_id\"] = []\n        series_dict[\"series_description\"] = []\n        series_dict[\"patient_position\"] = []\n        series_dict[\"image_position\"] = []\n        series_dict[\"image_orientation\"] = []\n        series_dict[\"slice_spacing\"] = []\n        series_dict[\"slice_thickness\"] = []\n        series_dict[\"rows\"] = []\n        series_dict[\"columns\"] = []\n        series_dict[\"photometric_interpretation\"] = []\n        series_dict[\"pixel_spacing\"] = []\n        series_dict[\"instance_number\"] = []\n\n        gc.collect()\n        del series_dict\n    return dicom_meta_df","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:22.646704Z","iopub.execute_input":"2024-09-25T12:24:22.647031Z","iopub.status.idle":"2024-09-25T12:24:22.662195Z","shell.execute_reply.started":"2024-09-25T12:24:22.647002Z","shell.execute_reply":"2024-09-25T12:24:22.660921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dicom_meta_df = read_dicom_meta()\n# dicom_meta_df.to_csv(\"dicom_meta.csv\")\nshutil.copy(\"/kaggle/input/dicom-meta/dicom_meta.csv\", \"./dicom_meta.csv\")\ndicom_meta_df = pd.read_csv(\"dicom_meta.csv\",\n                            converters={\"image_position\": eval,\n                                        \"image_orientation\": eval\n                                       }, \n                            index_col=0\n                           )","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:22.663577Z","iopub.execute_input":"2024-09-25T12:24:22.664510Z","iopub.status.idle":"2024-09-25T12:24:28.634770Z","shell.execute_reply.started":"2024-09-25T12:24:22.664476Z","shell.execute_reply":"2024-09-25T12:24:28.633584Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_meta_series_df = dicom_meta_df.merge(train_series_df[[\"study_id\", \"series_id\", \"series_description\"]],\n                                           on=[\"study_id\", \"series_id\"]).rename({\"series_description_y\": \"series_description_csv\",\n                                                            \"series_description_x\": \"series_description_dicom\"},axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:28.636068Z","iopub.execute_input":"2024-09-25T12:24:28.636428Z","iopub.status.idle":"2024-09-25T12:24:28.732993Z","shell.execute_reply.started":"2024-09-25T12:24:28.636400Z","shell.execute_reply":"2024-09-25T12:24:28.731893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Patient position","metadata":{}},{"cell_type":"markdown","source":"This is a scheme with the variants of patient positions relative to the equipment:","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:504a33bb-cf31-4bb8-af12-efdd744b915c.png)","metadata":{},"attachments":{"504a33bb-cf31-4bb8-af12-efdd744b915c.png":{"image/png":"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"}}},{"cell_type":"code","source":"\nsns.countplot(dicom_meta_series_df.groupby(\"study_id\")[[\"patient_position\"]].agg(\"max\"),\n              x=\"patient_position\"\n             )\nplt.ylabel(\"Number of series\")\nplt.xlabel(\"Patient position\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:28.734308Z","iopub.execute_input":"2024-09-25T12:24:28.734642Z","iopub.status.idle":"2024-09-25T12:24:29.096173Z","shell.execute_reply.started":"2024-09-25T12:24:28.734613Z","shell.execute_reply":"2024-09-25T12:24:29.095107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In $\\sim 80\\%$ of series the position of patient is \"Head fist - Supine\" and in other series is \"Feet fist -Supine\".","metadata":{}},{"cell_type":"markdown","source":"## Image position and orientation","metadata":{}},{"cell_type":"markdown","source":"This is a scheme showing the different types of slices used in anatomical studies:","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:4ebb7bea-9415-45aa-80df-d125dcba4e75.png)","metadata":{},"attachments":{"4ebb7bea-9415-45aa-80df-d125dcba4e75.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<span style=\"color:red\">Sagittal plane</span>.\n\n<span style=\"color:green\">Axial plane</span>.\n\n<span style=\"color:blue\">Coronal plane</span>.\n\nIn this dataset only the first two are present.","metadata":{"execution":{"iopub.status.busy":"2024-08-31T17:59:24.976918Z","iopub.execute_input":"2024-08-31T17:59:24.977840Z","iopub.status.idle":"2024-08-31T17:59:26.162039Z","shell.execute_reply.started":"2024-08-31T17:59:24.977801Z","shell.execute_reply":"2024-08-31T17:59:26.160439Z"}}},{"cell_type":"markdown","source":"This is thr schema with the names of axes in 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"}}},{"cell_type":"markdown","source":"### Series types","metadata":{"execution":{"iopub.status.busy":"2024-09-01T09:36:37.416975Z","iopub.execute_input":"2024-09-01T09:36:37.417390Z","iopub.status.idle":"2024-09-01T09:36:37.422962Z","shell.execute_reply.started":"2024-09-01T09:36:37.417358Z","shell.execute_reply":"2024-09-01T09:36:37.421659Z"}}},{"cell_type":"code","source":"def plot_proj():\n    fig = plt.figure(figsize=(15,5))\n    ax = fig.add_subplot(1,3,1)\n    ax.set_title(\"The number of studies\\nwith a certain number of series,\\nbroken down by type of projection\")\n    gr_dat = dicom_meta_series_df.groupby([\"study_id\", \"series_description_csv\"])[\"series_id\"].nunique().reset_index(level=1)\n    gr_dat.index.name\n    sns.countplot(data=gr_dat,\n                  x=\"series_id\",\n                  hue=\"series_description_csv\",\n                  ax=ax,\n                  palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                  edgecolor=\"black\",\n                  linestyle=\"-\",\n                 linewidth=0.3)\n    plt.xlabel(\"Number of series per study\")\n    plt.ylabel(\"Frequency\")\n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=8)\n\n    ax = fig.add_subplot(1,3,2)\n    ax.set_title(\"Number of studies\\n for certain projection\")\n    gr_data = dicom_meta_series_df.groupby(\"series_description_csv\")[[\"study_id\", \"series_id\"]].nunique().reset_index()\n    gr_data.index.name = None\n    sns.barplot(gr_data,\n                x=\"series_description_csv\",\n                y=\"study_id\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3,\n                legend=False) \n\n    ax.set_title(\"Number of studies and series (--)\\n for certain projection\")\n    sns.barplot(gr_data,\n                x=\"series_description_csv\",\n                y=\"series_id\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"--\",\n                linewidth=1.5,\n                legend=False) \n    for i, bar in enumerate(ax.patches):\n        if i < len(np.unique(gr_data[\"series_description_csv\"])):\n            x = bar.get_x()\n            width = bar.get_width()/2\n            bar.set_x(x)\n            bar.set_width(width)\n        else:\n            x = bar.get_x()\n            width = bar.get_width()/2\n            bar.set_x(x+width)\n            bar.set_width(width)\n    ax.set_ylabel(\"Number of series\")\n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=10)\n\n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Number of studies/series\")\n\n\n    ax = fig.add_subplot(1,3,3)\n    ax.set_title(\"Number of images\\n for certain projection (total and mean per series(--))\")\n    gr_data = dicom_meta_series_df.groupby(\"series_description_csv\")[\"series_id\"].count().reset_index()\n    gr_data.index.name = None\n    sns.barplot(gr_data,\n                x=\"series_description_csv\",\n                y=\"series_id\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3,\n                legend=False) \n\n    ax2 = plt.twinx(ax)\n    gr_data = dicom_meta_series_df.groupby([\"study_id\", \"series_id\", \"series_description_csv\"])['image_position'].agg(\"count\")\\\n    .groupby(level=2, axis=0).mean().reset_index()\n\n    sns.barplot(gr_data,\n                x=\"series_description_csv\",\n                y=\"image_position\",\n                hue=\"series_description_csv\",\n                ax=ax2,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"--\",\n                linewidth=1.5,\n                legend=False)  \n    for i, bar in enumerate(ax.patches):\n            x = bar.get_x()\n            width = bar.get_width()/2\n            bar.set_x(x)\n            bar.set_width(width)\n    for i, bar in enumerate(ax2.patches):\n            x = bar.get_x()\n            width = bar.get_width()/2\n            bar.set_x(x+width)\n            bar.set_width(width)\n\n    ax.set_ylabel(\"Number of series\")\n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=10, fmt='%.1f')\n    for cont in ax2.containers:\n        ax2.bar_label(cont, fontsize=10, fmt='%.1f')\n\n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Number of images\")\n    \n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:29.097833Z","iopub.execute_input":"2024-09-25T12:24:29.098292Z","iopub.status.idle":"2024-09-25T12:24:29.116594Z","shell.execute_reply.started":"2024-09-25T12:24:29.098258Z","shell.execute_reply":"2024-09-25T12:24:29.115447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_proj()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:29.122206Z","iopub.execute_input":"2024-09-25T12:24:29.122971Z","iopub.status.idle":"2024-09-25T12:24:30.435569Z","shell.execute_reply.started":"2024-09-25T12:24:29.122937Z","shell.execute_reply":"2024-09-25T12:24:30.434416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can conclude that:\n* Almost all studies include all three types of projections.\n    * The **Sagittal T1** projection typically **has only one series** per study, **similar to the Sagittal T2** projection, which has only a few studies with two series.\n    * Some studies contain **two or three series** with the **Axial** projection. However, the **majority ($\\sim75\\%$) have a single series**, as is the case with the Sagittal projection.\n    * The mean number of images per series is $17$ for **Sagittal** projections and $34.2$ for **Axial** projection.\n\nAs a result, we have for the **axial projection much more images**, more than twice that each saggital.","metadata":{}},{"cell_type":"markdown","source":"### Image orientation","metadata":{}},{"cell_type":"markdown","source":"Now consider distribution of images by x, y and z coordinates of it's first voxel. See more about 'Image Position' DICOM-parameter here: https://dicom.innolitics.com/ciods/ct-image/image-plane/00200032","metadata":{}},{"cell_type":"code","source":"def plot_coords():\n    fig, axes = plt.subplots(3,1,figsize=(15,15))\n    fig.suptitle(\"Distribution of image position coordinates\\nbroken down by projectiion type\",\n                fontsize=16)\n\n    j=0\n    for large_axes, projection, color in zip(axes, ['Axial T2', 'Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n\n        large_axes.set_title(projection + '\\n')\n        large_axes.axis(\"off\")\n        for i, angle_axis in enumerate([\"X\",\"Y\", \"Z\"]):\n            ax = fig.add_subplot(3,3,i +j*3+1)\n            (dicom_meta_series_df.query(\"series_description_csv == @projection\")\\\n            ['image_position'].apply(lambda x: x[i])).hist(bins=50, color=color)\n            ax.set_title(angle_axis)\n            ax.legend(title = angle_axis + \"\\n\" + projection)\n        j+=1\n\n    plt.show()\n\n    fig = plt.figure(figsize=(15,5))\n    fig.suptitle(\"Distribution of image position coordinates\\n(projection agnostic)\",\n                fontsize=16)\n    plt.subplot(1,3,1)\n    dicom_meta_df['image_position'].apply(lambda x: x[0]).hist(bins=30)\n    plt.subplot(1,3,2)\n    dicom_meta_df['image_position'].apply(lambda x: x[1]).hist(bins=30)\n    plt.subplot(1,3,3)\n    dicom_meta_df['image_position'].apply(lambda x: x[2]).hist(bins=30)\n    \n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T13:10:47.051235Z","iopub.execute_input":"2024-09-25T13:10:47.052114Z","iopub.status.idle":"2024-09-25T13:10:47.062960Z","shell.execute_reply.started":"2024-09-25T13:10:47.052072Z","shell.execute_reply":"2024-09-25T13:10:47.061738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='image_position'></a>","metadata":{}},{"cell_type":"code","source":"plot_coords()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T13:11:22.522974Z","iopub.execute_input":"2024-09-25T13:11:22.523422Z","iopub.status.idle":"2024-09-25T13:11:26.281606Z","shell.execute_reply.started":"2024-09-25T13:11:22.523387Z","shell.execute_reply":"2024-09-25T13:11:26.280484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are interesting bi-modality in disrtibution by $Z$-coordinate. Should explore it more. May be it relate with patient position: head first or feet first.","metadata":{}},{"cell_type":"code","source":"fig = plt.figure(figsize=(15,5))\n\nfor i, (projection, color) in enumerate(zip(['Axial T2', 'Sagittal T1', 'Sagittal T2/STIR'],[\"lightgreen\", \"cyan\", 'lightseagreen'])):\n    ax = fig.add_subplot(1,3,i+1)\n    slice_data = dicom_meta_series_df.query(\"series_description_csv == @projection\")\n    slice_data['image_position'] = slice_data['image_position'].apply(lambda x: x[2])\n    sns.histplot(slice_data, x='image_position', hue='patient_position', color=color)\n    ax = plt.gca()\n    ax.set_title(angle_axis)","metadata":{"execution":{"iopub.status.busy":"2024-09-25T13:40:58.411570Z","iopub.execute_input":"2024-09-25T13:40:58.412089Z","iopub.status.idle":"2024-09-25T13:41:01.133829Z","shell.execute_reply.started":"2024-09-25T13:40:58.412034Z","shell.execute_reply":"2024-09-25T13:41:01.132791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All images with **FFS** patient position belong to the **right distribution mode**. But images with **HFS** position belong to **both modes**.","metadata":{}},{"cell_type":"markdown","source":"### Image orientation","metadata":{}},{"cell_type":"code","source":"dicom_meta_series_df[\"slice_thickness\"] = np.round(dicom_meta_series_df[\"slice_thickness\"].astype(\"float\"),1)\ndicom_meta_series_df[\"slice_spacing\"] = np.round(dicom_meta_series_df[\"slice_spacing\"].astype(\"float\"),1)\n\ndef plot_angles(row_col):\n    fig, axes = plt.subplots(3,1,figsize=(15,15))\n    \n    if row_col==\"row\":\n        fig.suptitle(\"\")\n        j=0\n    elif row_col==\"column\":\n        j=3 \n    else:\n        raise ValueError(\"row_col argument must be one of following: 'row', 'column'\")\n    for large_axes, angle_axis in zip(axes,[\"X\",\"Y\", \"Z\"] ):\n\n        large_axes.set_title(angle_axis + '\\n')\n        large_axes.axis(\"off\")\n        i=0\n        for projection, color in zip(['Axial T2','Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n            ax = fig.add_subplot(3,3,i +j%3*3+1)\n            \n            data = dicom_meta_series_df.query(\"series_description_csv == @projection\")\\\n            ['image_orientation'].apply(lambda x: acos(x[j]))*180/np.pi\n            data.hist(bins=50, color=color)\n            \n            ax.set_ylim([0,30e3])\n            \n            if i==0 and j==1:\n                text_x = 0.1\n            else:\n                text_x = 0.55\n                \n            ax.text(text_x, 0.5,\n            f\"\"\"Min: {data.min():.1f},\\nMax: {data.max():.2f},\\\n    \\n$1\\%$: {data.quantile(0.01):.2f},\\n$5\\%$: {data.quantile(0.05):.2f},\\nMedian: {data.median():.2f},\\\n    \\nMean: {data.mean():.2f},\\n$75\\%$: {data.quantile(0.75):.2f},\\n$95\\%$: {data.quantile(0.95):.2f},\\n$99\\%$: {data.quantile(0.99):.2f}\"\"\",\n            bbox=dict(facecolor=color, alpha=0.5),\n            transform=ax.transAxes)\n            \n            ax.set_title(projection)\n            i+=1\n        j+=1\n\n    plt.show()\n\ndef angle_std(vals, coord):\n    return np.std([acos(val[coord])*180/np.pi for val in vals])\n    \ndef angle_range(vals, coord):\n    data = [acos(val[coord])*180/np.pi for val in vals]\n    return np.max(data) - np.min(data) \n\n    \ndef plot_angles_std(row_col):\n\n    fig = plt.figure(figsize=(15,10))\n    fig.suptitle(\"Mean standard deviation for image orientations angles in one seires\")\n    \n    if row_col==\"row\":\n        j=0\n    elif row_col==\"column\":\n        j=3 \n    else:\n        raise ValueError(\"row_col argument must be one of following: 'row', 'column'\")\n    \n    \n    \n    for i, angle_axis in enumerate([\"X\",\"Y\", \"Z\"]):\n        ax = plt.subplot(2,3,i+1)\n        data = dicom_meta_df.groupby(\"series_id\")['image_orientation'].agg(angle_std, coord=i+j)\n        data.hist(range=(0,10), bins=100)\n        ax.set_title(angle_axis)\n        ax.text(0.55, 0.6,\n                f\"\"\"Min: {data.min():.1f},\\nMax: {data.max():.2f},\\nMedian: {data.median():.2f},\\\n        \\nMean: {data.mean():.2f},\\n$75\\%$: {data.quantile(0.75):.2f},\\n$95\\%$: {data.quantile(0.95):.2f},\\n$99\\%$: {data.quantile(0.99):.2f}\"\"\",\n                bbox=dict(facecolor='blue', alpha=0.5),\n                transform=ax.transAxes)\n        \n    for i, angle_axis in enumerate([\"X\",\"Y\", \"Z\"]):\n        ax = plt.subplot(2,3,i+1+3)\n        ax.set_ylim([0,6e3])\n        data = dicom_meta_df.groupby(\"series_id\")['image_orientation'].agg(angle_range, coord=i+j)\n        data.hist(range=(0,10), bins=100)\n        ax.set_title(angle_axis)\n        ax.text(0.55, 0.6,\n                f\"\"\"Min: {data.min():.1f},\\nMax: {data.max():.2f},\\nMedian: {data.median():.2f},\\\n        \\nMean: {data.mean():.2f},\\n$75\\%$: {data.quantile(0.75):.2f},\\n$95\\%$: {data.quantile(0.95):.2f},\\n$99\\%$: {data.quantile(0.99):.2f}\"\"\",\n                bbox=dict(facecolor='blue', alpha=0.5),\n                transform=ax.transAxes)\n    plt.show()\n    \n    ","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:34.115185Z","iopub.execute_input":"2024-09-25T12:24:34.115623Z","iopub.status.idle":"2024-09-25T12:24:34.139564Z","shell.execute_reply.started":"2024-09-25T12:24:34.115583Z","shell.execute_reply":"2024-09-25T12:24:34.138440Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's look at the sum of angles for row of image and the same for the column.","metadata":{}},{"cell_type":"code","source":"plt.title(\"The row\")\ndicom_meta_df['image_orientation'].apply(lambda x: [acos(y)*180/np.pi for y in x]).apply(lambda x: x[0]+x[1]+x[2]).hist(bins=100)\nplt.xlabel(\"Number of images\")\nplt.xlabel(\"Sum of all angles\")\nplt.show()\n\nplt.title(\"The column\")\ndicom_meta_df['image_orientation'].apply(lambda x: [acos(y)*180/np.pi for y in x]).apply(lambda x: x[3]+x[4]+x[5]).hist(bins=100)\nplt.xlabel(\"Number of images\")\nplt.xlabel(\"Sum of all angles\")\nplt.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:34.140930Z","iopub.execute_input":"2024-09-25T12:24:34.141295Z","iopub.status.idle":"2024-09-25T12:24:36.157619Z","shell.execute_reply.started":"2024-09-25T12:24:34.141266Z","shell.execute_reply":"2024-09-25T12:24:36.156540Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It seems strange to me, but the sum of angles in all directions does not always equal exactly $180^\\circ$ or $360^\\circ$. Especialy for the column (obviously, for Axial projection, see picture below↓), in many cases, it is more than $200^\\circ$.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:eb0c2a13-ce1e-4acf-874d-d160ef39cad0.png)","metadata":{},"attachments":{"eb0c2a13-ce1e-4acf-874d-d160ef39cad0.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Let's take a closer look at the angle distributions for different projection types.","metadata":{}},{"cell_type":"code","source":"plot_angles(\"row\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:36.159223Z","iopub.execute_input":"2024-09-25T12:24:36.159678Z","iopub.status.idle":"2024-09-25T12:24:39.323794Z","shell.execute_reply.started":"2024-09-25T12:24:36.159638Z","shell.execute_reply":"2024-09-25T12:24:39.321802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_angles(\"column\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:39.325185Z","iopub.execute_input":"2024-09-25T12:24:39.326191Z","iopub.status.idle":"2024-09-25T12:24:42.308424Z","shell.execute_reply.started":"2024-09-25T12:24:39.326148Z","shell.execute_reply":"2024-09-25T12:24:42.307388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From the graphs above we can conclude that: \n* For **Axial** projection: \n    * rows of image are near parallel to the \"X\" axis and orthogonal others. \n    * columns are parallel the \"Y\" axis and orthogonal others.  \n    * so image lay in plane near **parallel the \"XY\" plane**.\n* For **Sagittal** projections \n    * rows of image are parallel the \"Y\" axis and orthogonal others.\n    * columns are parallel the \"Z\" axis and orthogonal others.\n    * so image lay in plane near **parallel the \"YZ\" plane**.\n    \nIt looks like the \"X\" and \"Y\" axes are reversed compared to the image with colored planes above.\nAnd it is agree with the DICOM documentations:\n\n_\"If Anatomical Orientation Type (0010,2210) is absent or has a value of BIPED, the **x-axis is increasing to the left hand side** of the patient. The **y-axis is increasing to the posterior side** of the patient. The z-axis is increasing toward the head of the patient.\"_\n\nSee here: https://dicom.innolitics.com/ciods/ct-image/image-plane/00200037","metadata":{}},{"cell_type":"code","source":"plot_angles_std(\"row\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:42.309975Z","iopub.execute_input":"2024-09-25T12:24:42.310437Z","iopub.status.idle":"2024-09-25T12:24:46.945170Z","shell.execute_reply.started":"2024-09-25T12:24:42.310397Z","shell.execute_reply":"2024-09-25T12:24:46.944080Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_angles_std(\"column\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:46.946603Z","iopub.execute_input":"2024-09-25T12:24:46.946917Z","iopub.status.idle":"2024-09-25T12:24:51.341526Z","shell.execute_reply.started":"2024-09-25T12:24:46.946890Z","shell.execute_reply":"2024-09-25T12:24:51.340417Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Almost all series have a standard deviation of **row** angles less than  $2.5^\\circ$ for the $X$-direction, $1.1^\\circ$ for the $Y$-direction, and  $1.6^\\circ$ for the $Z$-direction.\n\nAs excpected, ranges of angles have the larger $95$-th and $99$-th percentiles for the all directions, but the $75$-th percentile is zero and the mean is still less then $1^\\circ$\n\nFor the column angles we have a similar picture but for \"Y\" and \"Z\" direction there are much more stronger outliers: $95$-th percentiles of range are $28^\\circ$ and $41^\\circ$ accordingly. Although $75$-th percentile remains zero.\n \nWe can conclude that the variation in image orientation within a single series is relatively small, in the most of cases. But there are $\\sim20\\%$ of series with significant difference in column angles in \"Y\" and \"Z\" direction. ","metadata":{}},{"cell_type":"markdown","source":"## Slice thickness and spacing","metadata":{}},{"cell_type":"code","source":"def plot_slice():\n    fig, axes = plt.subplots(2, 1, figsize=(15,10))\n    axes[0].set_title(\"\")\n    axes[0].axis(\"off\")\n    # for i, projection in enumerate(['Sagittal T2/STIR', 'Sagittal T1', 'Axial T2']):\n    #     ax = fig.add_subplot(2,3,i+1)\n    #     ax.set_title(projection)\n    #     dicom_meta_series_df.groupby([\"study_id\", \"series_description_y\"])[\"series_id\"].nunique()\\\n    #     .loc[(slice(None,None),projection)].hist()\n    \n\n    axes[1].axis(\"off\")\n    ax = fig.add_subplot(2,3,1)\n\n    gr_data = dicom_meta_series_df.groupby([ \"slice_thickness\",\"series_description_csv\"])['image_position'].agg(\"count\").sort_index(level=1).reset_index()\n    ax.set_title(\"Slice thickness distribution\")\n    sns.barplot(gr_data,\n                x=\"slice_thickness\",\n                y=\"image_position\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Number of images\")\n\n    axes[0].axis(\"off\")\n    ax = fig.add_subplot(2,3,2)\n    gr_data_1 = dicom_meta_series_df.groupby([\"study_id\", \"series_description_csv\"])[\"slice_thickness\"].nunique().sort_index(level=1).reset_index()\n\n    ax.set_title(\"The number of thickness values for each study,\\nbroken down by projection type \")\n    sns.countplot(gr_data_1,\n                x=\"slice_thickness\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n\n\n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"The number of thickness values for each study\")\n\n    ax = fig.add_subplot(2,3,3)\n    ax.set_title(\"The number of thickness values for each study\")\n    gr_data_2 = dicom_meta_series_df.groupby([\"study_id\"])[\"slice_thickness\"].nunique().reset_index()\n    sns.countplot(gr_data_2,\n                x=\"slice_thickness\",\n                ax=ax,\n                palette=[\"blue\"],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=10)\n    ax.set_ylabel(\"The number of thickness values for each study\")\n    \n    axes[1].axis(\"off\")\n\n    ax = fig.add_subplot(2,3,4)\n    gr_data_1 = dicom_meta_series_df.groupby([\"study_id\", \"series_description_csv\"])[\"slice_spacing\"].nunique().sort_index(level=1).reset_index()\n\n    ax.set_title(\"The number of spacing values for each study,\\nbroken down by projection type \")\n    sns.countplot(gr_data_1,\n                x=\"slice_spacing\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n\n    ax = fig.add_subplot(2,3,5)\n    gr_data = dicom_meta_series_df.groupby([\"study_id\",\"series_id\", \"series_description_csv\"])[\"slice_spacing\"].nunique().sort_index(level=2).reset_index()\n\n    ax.set_title(\"The number of spacing values per series,\\nbroken down by projection type \")\n    sns.countplot(gr_data,\n                x=\"slice_spacing\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n\n    \n    ax = fig.add_subplot(2,3,6)\n    ax.set_title(\"The number of spacing values for each study.\")\n    gr_data = dicom_meta_series_df.groupby([\"study_id\"])[\"slice_spacing\"].nunique().reset_index()\n    sns.countplot(gr_data,\n                x=\"slice_spacing\",\n                ax=ax,\n                palette=[\"blue\"],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3\n               ) \n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=10)\n\n\n    plt.show()\n\n    fig, axes = plt.subplots(1,3,figsize=(15,5))\n\n    i=0\n    for projection, color in zip(['Axial T2','Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n        axes[i].axis(\"off\")\n  \n        ax = fig.add_subplot(1,3,i+1)\n\n        data = dicom_meta_series_df.query(\"series_description_csv == @projection\")\\\n        ['slice_spacing']\n        sns.histplot(data, \n                     bins=int((data.max()-data.min())*10), \n                     color=color,  \n                     edgecolor=\"black\",\n                     linestyle=\"-\",\n                     linewidth=0.3)\n        i+=1\n    \n    \n    fig, axes = plt.subplots(1,3,figsize=(15,5))\n    axes[0].axis(\"off\")\n    axes[1].axis(\"off\")\n    axes[2].axis(\"off\")\n    ax = fig.add_subplot(1,3,2)\n    ax.set_title(\"Series description from Dicom and csv relation\")\n    sns.countplot(dicom_meta_series_df.sort_values(by=\"series_description_csv\"),\n                x=\"series_description_dicom\",\n                hue=\"series_description_csv\",\n                ax=ax,\n                palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                edgecolor=\"black\",\n                linestyle=\"-\",\n                linewidth=0.3) \n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Number of images\")\n    for cont in ax.containers:\n        ax.bar_label(cont, fontsize=10)\n\n    plt.tight_layout()\n    plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:51.343379Z","iopub.execute_input":"2024-09-25T12:24:51.343787Z","iopub.status.idle":"2024-09-25T12:24:51.367322Z","shell.execute_reply.started":"2024-09-25T12:24:51.343754Z","shell.execute_reply":"2024-09-25T12:24:51.366241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_slice()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:51.368613Z","iopub.execute_input":"2024-09-25T12:24:51.368940Z","iopub.status.idle":"2024-09-25T12:24:54.999735Z","shell.execute_reply.started":"2024-09-25T12:24:51.368913Z","shell.execute_reply":"2024-09-25T12:24:54.998671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Each line of the graphs gives us the following conclusions:\n1. :\n    * **Most images** in all projections have **thickenss** $4$\n    * **All series** except several in Axial projection have **only one** value of slice **thickness**.\n    * $\\sim 57\\%$ of studies have $1$ **thickness** value for all series and $\\sim 43\\%$ have $2$, less then $1\\%$ has 3 values.\n2. :\n    * Sagittal images have only one slice spacing value per study (obviously, it not change during series for sagittal — all studies have only one sagittal t1 and t2 series).\n    * For Axial projection some studies have several slice spacing values. It is noteworthy, that this is due to different spacing between slices inside one series.\n    * Seems that spacing values for Sagittal T1 and T2 are the same.\n    * Most of studies has $2$ spacing values. Obviously, one for Axial and 1 for Sagittal projections. In some cases Axial and Sagittal spacing values are equal.\n    \n3. :\n    * T1 and T2 type of images noted correctly in csv file. Only few series have wrong values.\n    \nAbout difference between T1 and T2 images you can see, for example, here: https://www.radiologymasterclass.co.uk/tutorials/mri/t1_and_t2_images","metadata":{}},{"cell_type":"markdown","source":"## Labels related analysis","metadata":{}},{"cell_type":"code","source":"dicom_meta_series_df","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.001413Z","iopub.execute_input":"2024-09-25T12:24:55.001760Z","iopub.status.idle":"2024-09-25T12:24:55.028681Z","shell.execute_reply.started":"2024-09-25T12:24:55.001732Z","shell.execute_reply":"2024-09-25T12:24:55.027565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_meta_series_df[\"instance_number\"] = dicom_meta_series_df[\"instance_number\"].astype(\"int64\")","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.029910Z","iopub.execute_input":"2024-09-25T12:24:55.030239Z","iopub.status.idle":"2024-09-25T12:24:55.039645Z","shell.execute_reply.started":"2024-09-25T12:24:55.030206Z","shell.execute_reply":"2024-09-25T12:24:55.038551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_label_series_df = train_label_coords_df.merge(dicom_meta_series_df, on=[\"study_id\", \"series_id\", \"instance_number\"])","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.041270Z","iopub.execute_input":"2024-09-25T12:24:55.042289Z","iopub.status.idle":"2024-09-25T12:24:55.119194Z","shell.execute_reply.started":"2024-09-25T12:24:55.042237Z","shell.execute_reply":"2024-09-25T12:24:55.118130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Lrt's see are all studies and series present in df with labels.","metadata":{}},{"cell_type":"code","source":"set(train_series_df.study_id.astype(str)+\"_\" + train_series_df.series_id.astype(str)) -  set(train_label_coords_df.study_id.astype(str)+\"_\" + train_label_coords_df.series_id.astype(str))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.120401Z","iopub.execute_input":"2024-09-25T12:24:55.120748Z","iopub.status.idle":"2024-09-25T12:24:55.187767Z","shell.execute_reply.started":"2024-09-25T12:24:55.120718Z","shell.execute_reply":"2024-09-25T12:24:55.186745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"set(train_series_df.study_id.astype(str)) -  set(train_label_coords_df.study_id.astype(str))","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.189072Z","iopub.execute_input":"2024-09-25T12:24:55.189406Z","iopub.status.idle":"2024-09-25T12:24:55.221157Z","shell.execute_reply.started":"2024-09-25T12:24:55.189379Z","shell.execute_reply":"2024-09-25T12:24:55.220103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Only three series and one study are absent.","metadata":{}},{"cell_type":"code","source":"dicom_label_series_df","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.222466Z","iopub.execute_input":"2024-09-25T12:24:55.222829Z","iopub.status.idle":"2024-09-25T12:24:55.256062Z","shell.execute_reply.started":"2024-09-25T12:24:55.222800Z","shell.execute_reply":"2024-09-25T12:24:55.255071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Instance number","metadata":{}},{"cell_type":"code","source":"\nfig, axes = plt.subplots(2,3,figsize=(15,10))\nfig.suptitle(\"Instance number (order number, filename) of labeled images\\n broken down by projection\")\ni=0\nfor projection, color in zip(['Axial T2', 'Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n    axes[0,i].axis(\"off\")\n    i+=1\n    ax = fig.add_subplot(2,3,i)\n    ax.set_title(projection)\n    data = dicom_label_series_df.query(\"series_description_csv == @projection and instance_number<200\")[\"instance_number\"]\n    bins = (data.max() - data.min())\n    \n    dicom_label_series_df.query(\"series_description_csv == @projection and instance_number<200\")[\"instance_number\"].hist(\n                                                                                                bins=bins,\n                                                                                                ax=ax,\n                                                                                                color=color,\n                                                                                                edgecolor=\"black\",\n                                                                                                linestyle=\"-\",\n                                                                                                linewidth=0.3) \n    ax.set_xlim([0,60])\n    ax.set_xlabel(\"Instance number (filename)\")\n    ax.set_ylabel(\"Number of studies\")\n\n\nax = fig.add_subplot(2,3,5)\naxes[1,0].axis(\"off\")\naxes[1,1].axis(\"off\")\naxes[1,2].axis(\"off\")\nax.set_title(\"Number of studies with certain\\nquantity of labeled images per series\\n broken down by projection\")\ngr_data = dicom_label_series_df.groupby([\"study_id\",\"series_id\",\"series_description_csv\"])[\"instance_number\"].nunique()\\\n.reset_index().sort_values(by=\"series_description_csv\")\ngr_data.index.name = None\nsns.countplot(gr_data,\n            x=\"instance_number\",\n            hue=\"series_description_csv\",\n            ax=ax,\n            palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n            edgecolor=\"black\",\n            linestyle=\"-\",\n            linewidth=0.3,\n            ) \n\nax.set_xlabel(\"\")\nax.set_ylabel(\"Number of studies\")\n\n\nplt.tight_layout()\nplt.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:55.257303Z","iopub.execute_input":"2024-09-25T12:24:55.257613Z","iopub.status.idle":"2024-09-25T12:24:57.266767Z","shell.execute_reply.started":"2024-09-25T12:24:55.257588Z","shell.execute_reply":"2024-09-25T12:24:57.265781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"\\33[1mInstance number statistics parameters:\\33[0m\")\ndicom_label_series_df.groupby(\"series_description_csv\")[\"instance_number\"].describe(percentiles=[0.25,0.50,0.75,0.95,0.999])","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:57.268231Z","iopub.execute_input":"2024-09-25T12:24:57.268572Z","iopub.status.idle":"2024-09-25T12:24:57.302397Z","shell.execute_reply.started":"2024-09-25T12:24:57.268542Z","shell.execute_reply":"2024-09-25T12:24:57.301343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"\\33[1mAnomal instance number:\\33[0m\")\ndicom_label_series_df[dicom_label_series_df[\"instance_number\"]>200][[\"series_id\",\"instance_number\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:57.303745Z","iopub.execute_input":"2024-09-25T12:24:57.304104Z","iopub.status.idle":"2024-09-25T12:24:57.323735Z","shell.execute_reply.started":"2024-09-25T12:24:57.304076Z","shell.execute_reply":"2024-09-25T12:24:57.322672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All seems to be normal except some images for one series in Axial projection, that has anomal 5 thousands numbers (max number of images per series is 192).\nMay be, it is simple mistake and it would be enough to remove \"50\" prefix?","metadata":{}},{"cell_type":"markdown","source":"We can see the following things from first three graphs:\n* For Sagittal T1 there are 2 peaks in distribution: near $5-7$ and $12-13$. \n* For Sagittal T2 there is 1 peak near $9-11$. It seems like opposite to T1.\n* Distribution for Axiak projection is rather diffuse and spread with several not sharp peaks.\n\nLast graph tell us following:\n* The Sagittal T2 series typically contains the least number of labeled images, usually only $1$ or $2$ images per series.\n* The second place is taken by Sagittal T1 with $3-5$ images per series.\n* And the winner is... Axial T1! With huge $5-7$ images in average, and often $8-10$.","metadata":{}},{"cell_type":"markdown","source":"#### Image position","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(3,1,figsize=(15,15))\nfig.suptitle(\"Distribution of labeled image position coordinates\\nbroken down by projectiion type\",\n            fontsize=16)\n\nj=0\nfor large_axes, projection, color in zip(axes, ['Axial T2', 'Sagittal T1', 'Sagittal T2/STIR'], [\"lightgreen\", \"cyan\", 'lightseagreen']):\n\n    large_axes.set_title(projection + '\\n')\n    large_axes.axis(\"off\")\n    for i, angle_axis in enumerate([\"X\",\"Y\", \"Z\"]):\n        ax = fig.add_subplot(3,3,i +j*3+1)\n        (dicom_label_series_df.query(\"series_description_csv == @projection\").groupby([\"study_id\", \"series_id\", \"instance_number\"])\\\n        ['image_position'].agg(\"max\").apply(lambda x: x[i])).hist(bins=50, color=color)\n        ax.set_title(angle_axis)\n        ax.legend(title = angle_axis + \"\\n\" + projection)\n    j+=1\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:24:57.324942Z","iopub.execute_input":"2024-09-25T12:24:57.325312Z","iopub.status.idle":"2024-09-25T12:25:04.719600Z","shell.execute_reply.started":"2024-09-25T12:24:57.325284Z","shell.execute_reply":"2024-09-25T12:25:04.718503Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see some interesting things:\n* For Axial projection in $Z$-direction (in what images are stacked) distribution modes appear to become slightly more separated. This is mainly due to a decreasing fraction of images in the left mode. (compare to all images distribution, see [that graph](#image_position)).\n\n* For both Sagittal projection in $X$-direction (in what images are stacked) the distribution became more narrow ( see [the same graph](#image_position) to all images). For the Sagittal T1 projection, the distribution is split into two modes.","metadata":{}},{"cell_type":"markdown","source":" #### Condition type — level — image position","metadata":{}},{"cell_type":"code","source":"\n    \ndicom_label_series_df = dicom_label_series_df.assign(orth_coord=np.where(dicom_label_series_df[\"series_description_csv\"].str.contains(\"Axial\"),\n                                                                         dicom_label_series_df[\"image_position\"].apply(lambda x: x[2]),\n                                                                         dicom_label_series_df[\"image_position\"].apply(lambda x: x[0])\n                                                                        )\n                                                    )","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:25:04.720822Z","iopub.execute_input":"2024-09-25T12:25:04.721143Z","iopub.status.idle":"2024-09-25T12:25:04.801379Z","shell.execute_reply.started":"2024-09-25T12:25:04.721117Z","shell.execute_reply":"2024-09-25T12:25:04.800327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.style.use(\"seaborn-v0_8-whitegrid\")\nrp = sns.FacetGrid(dicom_label_series_df.sort_values(by=[\"series_description_csv\",\"level\"]),\n                   row=\"level\",\n                   col=\"series_description_csv\",\n                   hue=\"series_description_csv\", aspect=2, height=5.25,\n                   sharex=False,\n                   sharey=False) \n\nrp.map(plt.hist, 'orth_coord', clip_on=False, \n       alpha=0.7, bins=100) \n\nfor i in range(5):\n    rp.axes[i, 2].set_xlim([-100, 100])\n    rp.axes[i, 1].set_xlim([-100, 100])\n    rp.axes[i, 0].set_xlim([-800, 400])\n    for j in range(3):\n        rp.axes[i, j].set_xlabel(rp.axes[i, j].get_xlabel(), fontsize=24)\n        rp.axes[i, j].set_title(rp.axes[i, j].get_title(), fontsize=24)\n        \n# plt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:45:25.615290Z","iopub.execute_input":"2024-09-25T12:45:25.615706Z","iopub.status.idle":"2024-09-25T12:45:36.825162Z","shell.execute_reply.started":"2024-09-25T12:45:25.615673Z","shell.execute_reply":"2024-09-25T12:45:36.824107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As expected, the position of labels does not depend on the vertebra level for sagittal projections, but it does depend for axial projections: the lower the vertebra, the lower the $Z$-coordinate of the image.","metadata":{}},{"cell_type":"code","source":"plt.style.use(\"seaborn-v0_8-whitegrid\")\nrp = sns.FacetGrid(dicom_label_series_df.sort_values(by=\"series_description_csv\"),\n                   row=\"condition\",\n                   col=\"series_description_csv\",\n                   hue=\"series_description_csv\",\n                   aspect=2,\n                   height=5.25,\n                   palette=[\"lightgreen\", \"cyan\", 'lightseagreen'],\n                   sharex=False,\n                   sharey=False) \n  \nrp.map(plt.hist, 'orth_coord', clip_on=False, alpha=0.7, bins=100) \n  \n\nfor i in range(5):\n    rp.axes[i, 2].set_xlim([-100, 100])\n    rp.axes[i, 1].set_xlim([-100, 100])\n    rp.axes[i, 0].set_xlim([-800, 400])\n    for j in range(3):\n        rp.axes[i, j].set_xlabel(rp.axes[i, j].get_xlabel(), fontsize=24)\n        rp.axes[i, j].set_title(rp.axes[i, j].get_title().replace(\"|\",\"\\n\"), fontsize=24)\n    \nsns.set(font_scale=1.5)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-25T12:53:03.863112Z","iopub.execute_input":"2024-09-25T12:53:03.863900Z","iopub.status.idle":"2024-09-25T12:53:14.112415Z","shell.execute_reply.started":"2024-09-25T12:53:03.863863Z","shell.execute_reply":"2024-09-25T12:53:14.111299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we can label \"Spinal Canal Stenosis\" can be found only in Saggital T2 projection. Left and Right Foraminal Narrowing only in Saggital T1. And Left and Right Subartical Stenosis in Axial one.\n\nAs expected Left Foraminal Narrowing and Left Foraminal Narrowing  are located on opposite sides around zero.","metadata":{}},{"cell_type":"markdown","source":"As a result, we can draw the following conclusions: \n\n* The type of **projection** is uniquely **linked to** the type of medical **condition** that can be identified from the image. It might be useful to create **three models** for each condition type (spinal canal stenosis, left/right neural foraminal narrowing, left/right subarticular stenosis) and send the image to the appropriate model based on its projection type.\n* The distribution for $X$-coordinate of image position in **Axial** projection has different modes depend on **vertibra level** of label. So we can choose appropriate candidate images more precisely, based on their position, for the specific level at which we are checking for **subarticular stenosis** appearance.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}