{"library":"monai","title":"MONAI: Medical Open Network for AI","description":"MONAI (Medical Open Network for AI) is a PyTorch-based, open-source framework providing domain-optimized foundational capabilities for deep learning in healthcare imaging. It offers standardized, efficient, and reproducible components like data loaders, transforms, networks, and metrics, specifically designed for medical applications. The current version is 1.5.2, with regular minor and patch releases, typically multiple times per year.","language":"python","status":"active","last_verified":"Sun May 17","install":{"commands":["pip install monai"],"cli":null},"imports":["from monai.transforms import Compose","from monai.transforms import LoadImaged","from monai.data import CacheDataset","from monai.networks.nets import UNet","from monai.inferers import sliding_window_inference"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nimport numpy as np\nfrom monai.transforms import Compose, LoadImaged, EnsureChannelFirstd, ScaleIntensityRanged, Orientationd, Spacingd\nimport os\nimport nibabel as nib\n\n# Create dummy image file for demonstration\ndummy_image_path = \"dummy_image.nii.gz\"\nif not os.path.exists(dummy_image_path):\n    print(f\"Creating dummy NIfTI image at {dummy_image_path}...\")\n    dummy_data = np.random.rand(10, 10, 10).astype(np.float32)\n    affine = np.diag([1, 1, 1, 1])\n    nifti_img = nib.Nifti1Image(dummy_data, affine)\n    nib.save(nifti_img, dummy_image_path)\n\n# 1. Define a transform pipeline for dictionary-based data\nkeys = [\"image\"] # working with dictionary data, key for the image\ntransform = Compose(\n    [\n        LoadImaged(keys=keys), # Load medical image data\n        EnsureChannelFirstd(keys=keys), # Ensure channel dimension is first\n        ScaleIntensityRanged(keys=keys, a_min=0, a_max=1, b_min=0.0, b_max=1.0, clip=True), # Normalize intensity\n        Orientationd(keys=keys, axcodes=\"RAS\"), # Reorient image to standard anatomical space\n        Spacingd(keys=keys, pixdim=(1.5, 1.5, 2.0), mode=\"bilinear\"), # Resample to desired spacing\n    ]\n)\n\n# 2. Create dummy data list (mimicking a dataset of file paths)\ndata = [{\n    \"image\": dummy_image_path\n}] * 2 # Two dummy items for batching effect\n\n# 3. Apply transforms (typically done within a Dataset/DataLoader)\ntransformed_data = [transform(item) for item in data]\n\nprint(f\"Original image path: {data[0]['image']}\")\nprint(f\"Transformed image shape: {transformed_data[0]['image'].shape}\")\nprint(f\"Transformed image dtype: {transformed_data[0]['image'].dtype}\")\n\n# Clean up dummy file\nif os.path.exists(dummy_image_path):\n    os.remove(dummy_image_path)\n    print(f\"Cleaned up dummy NIfTI image: {dummy_image_path}\")","lang":"python","description":"This quickstart demonstrates a basic MONAI transform pipeline using dictionary-based transforms (suffixed with 'd'). It loads a dummy NIfTI image, applies several common preprocessing steps like channel reordering, intensity scaling, orientation standardization, and spatial resampling, then prints the shape and data type of the transformed image. This setup mirrors typical medical imaging data workflows.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-17","installed_version":"1.5.2","pypi_latest":"1.5.2","is_stale":false,"summary":{"python_range":"3.10–3.9","success_rate":40,"avg_install_s":70,"avg_import_s":7.73,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"monai","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":79.5,"import_time_s":5.35,"mem_mb":81.2,"disk_size":"4.7G"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"monai","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":72.9,"import_time_s":8.67,"mem_mb":89.1,"disk_size":"4.8G"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"monai","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":63.4,"import_time_s":10.14,"mem_mb":87.5,"disk_size":"4.8G"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"monai","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":64.1,"import_time_s":6.77,"mem_mb":89.9,"disk_size":"4.8G"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"monai","exit_code":1,"wheel_type":null,"failure_reason":"timeout","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null}]}}