nnunet

name	nnunet
description	No New U-Net — self-configuring framework for medical image segmentation. Automatically adapts to any dataset. Top performer on biomedical segmentation benchmarks (BraTS, KiTS, etc.).
tags	["medical-imaging","segmentation","unet","deep-learning","pytorch","zorai"]

Overview

nnUNet (No New U-Net) is a self-configuring framework for medical image segmentation that automatically adapts to any dataset. Consistently top-performing on benchmarks like BraTS, KiTS, and AMOS.

Installation

uv pip install nnunetv2

Plan and Preprocess

nnUNetv2_plan_and_preprocess -d DATASET_ID -pl nnUNetPlanner

Train

nnUNetv2_train DATASET_ID CONFIG 0  # CONFIG: 2d, 3d_fullres, 3d_lowres

Inference

nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c CONFIG

Python API

from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor

predictor = nnUNetPredictor()
predictor.initialize_from_trained_model_folder("nnUNet_results/DatasetXYZ", "3d_fullres")
predictor.predict_from_files("input_images", "output_segmentations")

Workflow

1. Prepare dataset in nnUNet format (imagesTr, labelsTr, dataset.json)
2. Run nnUNetv2_plan_and_preprocess for automatic configuration
3. Train with nnUNetv2_train
4. Predict with nnUNetv2_predict or Python API
5. Ensemble multiple configurations for best accuracy