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version: "1.0.0" name: monai description: "Medical Open Network for AI (MONAI). Framework for deep learning in medical imaging: segmentation, classification, detection, registration. Supports DICOM, NIfTI, PNG. Built on PyTorch with GPU acceleration." tags: [medical-imaging, deep-learning, pytorch, segmentation, radiology, dicom, zorai]

Overview

MONAI is the standard PyTorch-based framework for medical imaging deep learning. Use it for segmentation, classification, registration, preprocessing, and training pipelines on DICOM, NIfTI, and other medical image formats.

Installation

bash

uv pip install monai
# optional extras as needed
uv pip install monai[all]

Core strengths

MONAI gives you:

medical-image-aware transforms
domain-specific architectures like UNet, UNETR, SwinUNETR
losses like DiceLoss and DiceCELoss
metrics like Dice and Hausdorff distance
dataset and engine utilities for training loops

Basic 3D segmentation model

python

import torch
from monai.networks.nets import UNet
from monai.networks.layers import Norm
model = UNet(
    spatial_dims=3,
    in_channels=1,
    out_channels=3,
    channels=(16, 32, 64, 128, 256),
    strides=(2, 2, 2, 2),
    num_res_units=2,
    norm=Norm.BATCH,
).cuda()

Preprocessing transforms

python

from monai.transforms import (
    Compose, LoadImaged, EnsureChannelFirstd, Spacingd,
    Orientationd, ScaleIntensityRanged, CropForegroundd,
    RandCropByPosNegLabeld, RandFlipd, EnsureTyped,
)
train_transforms = Compose([
    LoadImaged(keys=['image', 'label']),
    EnsureChannelFirstd(keys=['image', 'label']),
    Orientationd(keys=['image', 'label'], axcodes='RAS'),
    Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.0), mode=('bilinear', 'nearest')),
    ScaleIntensityRanged(keys=['image'], a_min=-200, a_max=300, b_min=0.0, b_max=1.0, clip=True),
    CropForegroundd(keys=['image', 'label'], source_key='image'),
    RandCropByPosNegLabeld(keys=['image', 'label'], label_key='label', spatial_size=(96, 96, 96), num_samples=4),
    RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0),
    EnsureTyped(keys=['image', 'label']),
])

Loss + metric

python

from monai.losses import DiceCELoss
from monai.metrics import DiceMetric
loss_fn = DiceCELoss(to_onehot_y=True, softmax=True)
dice_metric = DiceMetric(include_background=False, reduction='mean')

Workflow

Normalize image orientation and spacing first.
Use label-safe transform modes: bilinear for images, nearest for labels.
Start with MONAI reference architectures before inventing custom ones.
Verify voxel spacing assumptions before training.
Track Dice per class, not just aggregate loss.
Save preprocessing config with the model so inference matches training.

All versions