{"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"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
RSNA 2022 Cervical Spine Fracture Detection
\n
Identify cervical fractures from scans
\n
\n
","metadata":{}},{"cell_type":"markdown","source":"The goal of this competition is to identify fractures in CT scans of the cervical spine (neck) at both the level of a single vertebrae and the entire patient. Quickly detecting and determining the location of any vertebral fractures is essential to prevent neurologic deterioration and paralysis after trauma\n\nThis competition uses a hidden test. When your submitted notebook is scored the actual test data (including a full length sample submission) will be made available to your notebook","metadata":{}},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')\n\nfrom matplotlib.patches import Rectangle\nimport numpy as np\nimport pandas as pd\nimport os\nimport re\nimport random\nimport matplotlib.pyplot as plt\nimport plotly\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nimport plotly.express as px\nfrom pydicom import dcmread\nfrom tqdm import tqdm\nimport multiprocessing as mp\nimport seaborn as sns\nimport datetime","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-08-01T21:34:49.309104Z","iopub.execute_input":"2022-08-01T21:34:49.310175Z","iopub.status.idle":"2022-08-01T21:34:51.511354Z","shell.execute_reply.started":"2022-08-01T21:34:49.310131Z","shell.execute_reply":"2022-08-01T21:34:51.509879Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Path to dataframe and image folders\nPATH = \"../input/rsna-2022-cervical-spine-fracture-detection\"\n\n# Import trainset\ntrain_dataframe = pd.read_csv(os.path.join(PATH, 'train.csv'))","metadata":{"execution":{"iopub.status.busy":"2022-08-01T21:35:24.133389Z","iopub.execute_input":"2022-08-01T21:35:24.133824Z","iopub.status.idle":"2022-08-01T21:35:24.150885Z","shell.execute_reply.started":"2022-08-01T21:35:24.133786Z","shell.execute_reply":"2022-08-01T21:35:24.149686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataframe","metadata":{"execution":{"iopub.status.busy":"2022-08-01T21:35:24.387911Z","iopub.execute_input":"2022-08-01T21:35:24.38875Z","iopub.status.idle":"2022-08-01T21:35:24.409409Z","shell.execute_reply.started":"2022-08-01T21:35:24.3887Z","shell.execute_reply":"2022-08-01T21:35:24.408007Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataframe.info()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:26.919859Z","iopub.execute_input":"2022-08-01T18:13:26.920692Z","iopub.status.idle":"2022-08-01T18:13:26.941435Z","shell.execute_reply.started":"2022-08-01T18:13:26.920638Z","shell.execute_reply":"2022-08-01T18:13:26.93961Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataframe.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:26.946315Z","iopub.execute_input":"2022-08-01T18:13:26.946777Z","iopub.status.idle":"2022-08-01T18:13:26.964535Z","shell.execute_reply.started":"2022-08-01T18:13:26.94674Z","shell.execute_reply":"2022-08-01T18:13:26.963177Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataframe.shape","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:26.96692Z","iopub.execute_input":"2022-08-01T18:13:26.967775Z","iopub.status.idle":"2022-08-01T18:13:26.980743Z","shell.execute_reply.started":"2022-08-01T18:13:26.967722Z","shell.execute_reply":"2022-08-01T18:13:26.979301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Scan and fracture counts per patient\ntrain_dataframe['nb_scans'] = train_dataframe['StudyInstanceUID'].apply(lambda x: len(os.listdir(f'../input/rsna-2022-cervical-spine-fracture-detection/train_images/{x}')))\ntrain_dataframe['nb_fractures'] = train_dataframe[['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7']].sum(axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:26.982941Z","iopub.execute_input":"2022-08-01T18:13:26.983472Z","iopub.status.idle":"2022-08-01T18:13:34.196339Z","shell.execute_reply.started":"2022-08-01T18:13:26.983423Z","shell.execute_reply":"2022-08-01T18:13:34.195267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.box(train_dataframe, y=\"nb_scans\", points=\"all\", title='Nb scans per patient', color_discrete_sequence=[\"goldenrod\"])\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-08-01T18:13:34.197855Z","iopub.execute_input":"2022-08-01T18:13:34.19898Z","iopub.status.idle":"2022-08-01T18:13:34.270623Z","shell.execute_reply.started":"2022-08-01T18:13:34.198939Z","shell.execute_reply":"2022-08-01T18:13:34.26919Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dict_c = {}\nfor i in range(1,7):\n dict_c[f'C{i}'] = train_dataframe.loc[train_dataframe[f'C{i}'] == 1].shape[0]\n \nx = list(dict_c.keys())\ny = list(dict_c.values())\n\nmax_index = np.argmax(list(dict_c.values()))\ncolors = ['lightblue',] * 7\ncolors[max_index] = 'goldenrod'\n\nfig = go.Figure(\n data=[\n go.Bar(\n x=x,\n y=y,\n marker_color=colors\n )\n ]\n)\n\nfig.update_layout(\n title_text='Fractured vertebrae location counts',\n)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-08-01T18:13:34.272561Z","iopub.execute_input":"2022-08-01T18:13:34.273069Z","iopub.status.idle":"2022-08-01T18:13:34.302391Z","shell.execute_reply.started":"2022-08-01T18:13:34.273017Z","shell.execute_reply":"2022-08-01T18:13:34.301138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"colors = ['lightblue',] * 6\ncolors[0] = 'goldenrod'\n\ndict_nb_fractures = dict(train_dataframe.loc[train_dataframe['nb_fractures'] != 0, 'nb_fractures'].value_counts())\n\nx = list(dict_nb_fractures.keys())\ny = list(dict_nb_fractures.values())\n\nfig = go.Figure(\n data=[\n go.Bar(\n x=x,\n y=y,\n marker_color=colors\n )\n ]\n)\n\nfig.update_layout(\n title_text='Fracture counts distribution',\n)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-08-01T18:13:34.304Z","iopub.execute_input":"2022-08-01T18:13:34.304413Z","iopub.status.idle":"2022-08-01T18:13:34.329897Z","shell.execute_reply.started":"2022-08-01T18:13:34.304376Z","shell.execute_reply":"2022-08-01T18:13:34.328616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Scan counts per nb fractures\ny0 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==1, 'nb_scans']\ny1 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==2, 'nb_scans']\ny2 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==3, 'nb_scans']\ny3 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==4, 'nb_scans']\ny4 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==5, 'nb_scans']\ny5 = train_dataframe.loc[train_dataframe[\"nb_fractures\"]==6, 'nb_scans']\n\nfig = go.Figure()\nfig.add_trace(go.Box(y=y0, name=\"1\", marker_color='lightblue'))\nfig.add_trace(go.Box(y=y1, name=\"2\", marker_color='lightblue'))\nfig.add_trace(go.Box(y=y2, name=\"3\", marker_color='goldenrod'))\nfig.add_trace(go.Box(y=y3, name=\"4\", marker_color='lightblue'))\nfig.add_trace(go.Box(y=y2, name=\"5\", marker_color='goldenrod'))\nfig.add_trace(go.Box(y=y3, name=\"6\", marker_color='lightblue'))\n\nfig.update_layout(\n title_text='Scan counts per nb fractures',\n showlegend=False\n)\nfig.update_xaxes(title = 'At least')\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-08-01T18:13:34.331388Z","iopub.execute_input":"2022-08-01T18:13:34.332238Z","iopub.status.idle":"2022-08-01T18:13:34.369513Z","shell.execute_reply.started":"2022-08-01T18:13:34.332173Z","shell.execute_reply":"2022-08-01T18:13:34.368294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Slightly higher median number of scans for those who have 3 or 5 fractures","metadata":{}},{"cell_type":"markdown","source":"# Read DICOM files","metadata":{}},{"cell_type":"markdown","source":"### What is a DICOM file?\n\nA DICOM file is an image saved in the Digital Imaging and Communications in Medicine (DICOM) format. It contains an image from a medical scan, such as an ultrasound or MRI. DICOM files may also include identification data for patients to link the image to a specific individual.","metadata":{}},{"cell_type":"code","source":"ds = dcmread(os.path.join(PATH, 'train_images', '1.2.826.0.1.3680043.10001/1.dcm'))\nds","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.371142Z","iopub.execute_input":"2022-08-01T18:13:34.372387Z","iopub.status.idle":"2022-08-01T18:13:34.387765Z","shell.execute_reply.started":"2022-08-01T18:13:34.372337Z","shell.execute_reply":"2022-08-01T18:13:34.386828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Extracting metadata from DICOM files","metadata":{}},{"cell_type":"code","source":"def extract_file(file_path):\n '''\n :param file_path: path of DICOM file to extract metadata from\n :return: a dictionary of extracted metadata\n '''\n \n ds = dcmread(file_path)\n image_id = file_path.split(sep=\"/\")[-2]\n\n observation_dict = {}\n observation_dict['image_id'] = image_id\n \n file_meta_keys = list(ds.file_meta._dict.keys())\n remaining_meta_keys = list(ds._dict.keys())\n \n for key in file_meta_keys:\n observation_dict[str(key)] = str(ds.file_meta[key].value)\n \n # Not taking into account pixel value\n for key in remaining_meta_keys:\n if key != (0x7fe0, 0x0010):\n observation_dict[str(key)] = str(ds[key].value)\n \n return observation_dict","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.389204Z","iopub.execute_input":"2022-08-01T18:13:34.389796Z","iopub.status.idle":"2022-08-01T18:13:34.398693Z","shell.execute_reply.started":"2022-08-01T18:13:34.389761Z","shell.execute_reply":"2022-08-01T18:13:34.397478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# An example\nextract_file('../input/rsna-2022-cervical-spine-fracture-detection/train_images/1.2.826.0.1.3680043.10001/1.dcm')","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.402822Z","iopub.execute_input":"2022-08-01T18:13:34.403471Z","iopub.status.idle":"2022-08-01T18:13:34.422085Z","shell.execute_reply.started":"2022-08-01T18:13:34.403432Z","shell.execute_reply":"2022-08-01T18:13:34.421052Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mapper_dict = {\n 'image_id':'image_id',\n \n '(0002, 0001)':\"File Meta Information Version\",\n '(0002, 0002)':\"Media Storage SOP Class UID\",\n '(0002, 0003)':\"Media Storage SOP Instance UID\",\n '(0002, 0010)':\"Transfer Syntax UID\",\n '(0002, 0012)':\"Implementation Class UID\",\n '(0002, 0013)':\"Implementation Version Name\",\n\n '(0008, 0018)':\"SOPInstanceUID\",\n '(0008, 0023)':\"Date of Creation\",\n '(0008, 0033)':\"Time of Creation\",\n\n '(0010, 0010)':\"Patient Name\",\n '(0010, 0020)':\"Patient ID\",\n \n '(0018, 0050)':\"Slice Thickness\",\n \n '(0020, 000d)':\"Study Instance UID\",\n '(0020, 000e)':\"Series Instance UID\",\n '(0020, 0013)':\"Instance Number\",\n '(0020, 0032)':\"Image Position (Patient)\",\n '(0020, 0037)':\"Image Orientation (Patient)\",\n \n '(0028, 0002)':\"Samples per Pixel\",\n '(0028, 0004)':\"Photometric Interpretation\",\n '(0028, 0010)':\"Rows\",\n '(0028, 0011)':\"Columns\",\n '(0028, 0030)':\"Pixel Spacing\",\n '(0028, 0100)':\"Bits Allocated\",\n '(0028, 0101)':\"Bits Stored\",\n '(0028, 0102)':\"High Bit\",\n '(0028, 0103)':\"Pixel Representation\",\n '(0028, 1050)':\"Window Center\",\n '(0028, 1051)':\"Window Width\",\n '(0028, 1052)':\"Rescale Intercept\",\n '(0028, 1053)':\"Rescale Slope\",\n}","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.42385Z","iopub.execute_input":"2022-08-01T18:13:34.424934Z","iopub.status.idle":"2022-08-01T18:13:34.434729Z","shell.execute_reply.started":"2022-08-01T18:13:34.424885Z","shell.execute_reply":"2022-08-01T18:13:34.433452Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def meta_information_one_folder(folder):\n '''\n :param folder: path of folder containing all DICOM files to extract metadata from\n :return: a dataframe with extracted metadata, one row matches one scan\n '''\n \n folder_filenames = os.listdir(os.path.join(PATH, folder))\n one_obs = extract_file(os.path.join(PATH, folder, folder_filenames[0]))\n metadata = pd.DataFrame(columns = one_obs.keys())\n \n print(f'Extracting metadata from folder {folder}')\n for filename in tqdm(folder_filenames):\n one_obs = extract_file(os.path.join(PATH, folder, filename))\n metadata = metadata.append(one_obs, ignore_index=True)\n \n metadata.columns = metadata.columns.map(mapper_dict)\n metadata.to_csv(f\"dicom_metadata.csv\", index=False)\n \n return metadata","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.436932Z","iopub.execute_input":"2022-08-01T18:13:34.437701Z","iopub.status.idle":"2022-08-01T18:13:34.453953Z","shell.execute_reply.started":"2022-08-01T18:13:34.43766Z","shell.execute_reply":"2022-08-01T18:13:34.452482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"metadata = meta_information_one_folder('train_images/1.2.826.0.1.3680043.10001')","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:34.455733Z","iopub.execute_input":"2022-08-01T18:13:34.456417Z","iopub.status.idle":"2022-08-01T18:13:37.321973Z","shell.execute_reply.started":"2022-08-01T18:13:34.456371Z","shell.execute_reply":"2022-08-01T18:13:37.320563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for column in metadata.columns:\n if len(set(metadata[column])) != 1:\n print(column)","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:37.323771Z","iopub.execute_input":"2022-08-01T18:13:37.324126Z","iopub.status.idle":"2022-08-01T18:13:37.335404Z","shell.execute_reply.started":"2022-08-01T18:13:37.324094Z","shell.execute_reply":"2022-08-01T18:13:37.334059Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Among all scans, only these columns have several values for a given patient","metadata":{}},{"cell_type":"markdown","source":"# Preprocessing metadata","metadata":{}},{"cell_type":"code","source":"metadata['Date of Creation'] = metadata['Time of Creation'].apply(lambda x: datetime.datetime.fromtimestamp(eval(x)).strftime('%Y-%m-%d %H:%M:%S'))\nmetadata[['Media Storage SOP Instance UID', 'SOPInstanceUID', 'Time of Creation', 'Date of Creation', 'Instance Number', 'Image Position (Patient)']].head()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:37.33752Z","iopub.execute_input":"2022-08-01T18:13:37.338013Z","iopub.status.idle":"2022-08-01T18:13:37.369406Z","shell.execute_reply.started":"2022-08-01T18:13:37.337968Z","shell.execute_reply":"2022-08-01T18:13:37.367115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Some observations","metadata":{}},{"cell_type":"code","source":"def get_random_files_from_patient(path):\n '''\n :param path: path of folder containing more than 9 DICOM files\n :return: a list of 9 DICOM filenames\n '''\n \n return random.sample(os.listdir(path), 9)\n\n\ndef rescale_image(dicom_file):\n '''\n :param dicom_file: DICOM file\n :return: image and rescaled image\n '''\n \n image = dicom_file.pixel_array.flatten()\n rescaled_image = image * dicom_file.RescaleSlope + dicom_file.RescaleIntercept\n \n return image, rescaled_image\n\n\ndef display_images(files_list, graph_indexes = np.arange(9)):\n '''\n :param files_list: list of DICOM files (output from get_random_files_from_patient)\n :param graph_indexes: indexes of the files to display\n :return: plots images, pixel distributions and rescaled pixel distributions\n '''\n \n # define subplot\n fig, axs = plt.subplots(3,3, figsize=(20,12))\n for idx, file in enumerate(files_list):\n \n full_path = os.path.join('../input/rsna-2022-cervical-spine-fracture-detection/train_images/1.2.826.0.1.3680043.10001', file)\n ds = dcmread(full_path)\n\n axs[idx//3, 0].imshow(ds.pixel_array, cmap=plt.get_cmap('gray')) \n axs[idx//3, 0].axis(\"off\")\n \n image, rescaled_image = rescale_image(ds)\n \n sns.distplot(image.flatten(), ax=axs[idx//3, 1]);\n sns.distplot(rescaled_image.flatten(), ax=axs[idx//3, 2])\n axs[idx//3, 1].set_title(\"Raw pixel array distributions\")\n axs[idx//3, 2].set_title(\"HU unit distributions\"); \n \n \n # the bottom of the subplots of the figure\n plt.subplots_adjust(bottom = 0.001)\n plt.subplots_adjust(top = 0.99)\n \n # show the figure\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:37.371401Z","iopub.execute_input":"2022-08-01T18:13:37.372617Z","iopub.status.idle":"2022-08-01T18:13:37.392795Z","shell.execute_reply.started":"2022-08-01T18:13:37.372563Z","shell.execute_reply":"2022-08-01T18:13:37.39124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"files_to_display = get_random_files_from_patient('../input/rsna-2022-cervical-spine-fracture-detection/train_images/1.2.826.0.1.3680043.10001')\ndisplay_images(files_to_display)","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:13:37.394422Z","iopub.execute_input":"2022-08-01T18:13:37.395638Z","iopub.status.idle":"2022-08-01T18:13:58.577443Z","shell.execute_reply.started":"2022-08-01T18:13:37.395575Z","shell.execute_reply":"2022-08-01T18:13:58.576008Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Bounding Boxes","metadata":{}},{"cell_type":"code","source":"bb_train = pd.read_csv('../input/rsna-2022-cervical-spine-fracture-detection/train_bounding_boxes.csv')\nbb_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T21:34:53.914183Z","iopub.execute_input":"2022-08-01T21:34:53.915411Z","iopub.status.idle":"2022-08-01T21:34:53.977813Z","shell.execute_reply.started":"2022-08-01T21:34:53.915362Z","shell.execute_reply":"2022-08-01T21:34:53.976971Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"bb_train.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"bb_train.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-08-01T18:18:47.846358Z","iopub.execute_input":"2022-08-01T18:18:47.846795Z","iopub.status.idle":"2022-08-01T18:18:47.860562Z","shell.execute_reply.started":"2022-08-01T18:18:47.84676Z","shell.execute_reply":"2022-08-01T18:18:47.859297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Counting total number of files for annotated patients\n# siuid_list = train_dataframe.loc[train_dataframe['patient_overall'] == 1, 'StudyInstanceUID'].values\n# cnt = 0\n# for siuid in tqdm(siuid_list):\n# cnt+=len(os.listdir(f'../input/rsna-2022-cervical-spine-fracture-detection/train_images/{siuid}'))","metadata":{"execution":{"iopub.status.busy":"2022-08-01T21:46:20.180141Z","iopub.execute_input":"2022-08-01T21:46:20.18144Z","iopub.status.idle":"2022-08-01T22:24:55.373993Z","shell.execute_reply.started":"2022-08-01T21:46:20.181396Z","shell.execute_reply":"2022-08-01T22:24:55.37274Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print(cnt)\n# 320919","metadata":{"execution":{"iopub.status.busy":"2022-08-01T22:24:55.376523Z","iopub.execute_input":"2022-08-01T22:24:55.377313Z","iopub.status.idle":"2022-08-01T22:24:55.382965Z","shell.execute_reply.started":"2022-08-01T22:24:55.377264Z","shell.execute_reply":"2022-08-01T22:24:55.381932Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# getting proportion of annotated files\n7217/320919","metadata":{"execution":{"iopub.status.busy":"2022-08-01T22:24:55.384558Z","iopub.execute_input":"2022-08-01T22:24:55.385823Z","iopub.status.idle":"2022-08-01T22:24:55.398893Z","shell.execute_reply.started":"2022-08-01T22:24:55.385772Z","shell.execute_reply":"2022-08-01T22:24:55.397511Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import glob\n# missing_annot = []\n# spines = {f\"C{i}\": [] for i in range(1, 8)}\n\n# for _, row in tqdm(train_dataframe.iterrows()):\n# study_id = row[\"StudyInstanceUID\"]\n# ls_imgs = glob.glob(os.path.join('../input/rsna-2022-cervical-spine-fracture-detection/train_images', study_id, \"*.dcm\"))\n# annot_slices = bb_train[bb_train[\"StudyInstanceUID\"] == study_id][\"slice_number\"].values.tolist()\n# if not annot_slices:\n# if row[\"patient_overall\"] == 1:\n# missing_annot.append(study_id)\n# continue\n \n# nb = float(len(ls_imgs))\n# idx = [i / nb for i in annot_slices]\n# for c in spines:\n# if row[c] == 1:\n# spines[c] += idx","metadata":{"execution":{"iopub.status.busy":"2022-08-01T21:39:38.466042Z","iopub.execute_input":"2022-08-01T21:39:38.466474Z","iopub.status.idle":"2022-08-01T21:40:58.711982Z","shell.execute_reply.started":"2022-08-01T21:39:38.466435Z","shell.execute_reply":"2022-08-01T21:40:58.710211Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Segmentations","metadata":{}},{"cell_type":"code","source":"# import nibabel as nib\n# img = nib.load('../input/rsna-2022-cervical-spine-fracture-detection/segmentations/1.2.826.0.1.3680043.10633.nii')\n# data = img.get_data()\n# plt.imshow(data, cmap=plt.get_cmap('gray')) ","metadata":{"execution":{"iopub.status.busy":"2022-08-01T19:15:07.981121Z","iopub.execute_input":"2022-08-01T19:15:07.98155Z","iopub.status.idle":"2022-08-01T19:15:09.950077Z","shell.execute_reply.started":"2022-08-01T19:15:07.981517Z","shell.execute_reply":"2022-08-01T19:15:09.948603Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## WIP...","metadata":{}},{"cell_type":"markdown","source":"# References\n\n#### DICOM files\n* Deal with .dicom files\n* Matching DICOM metadata\n\n#### Notebook\n* Rescaling DICOM files","metadata":{}},{"cell_type":"markdown","source":"
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Thank you for taking the time to read this notebook. I hope that I was able to answer your questions or your curiosity and that it was quite understandable. any constructive comments are welcome. They help me progress and motivate me to share better quality content. I am above all a passionate person who tries to advance my knowledge but also that of others. If you liked it, feel free to upvote and share my work.
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Thank you and may passion guide you.
","metadata":{}}]}