Package: nnunetv2
Welcome to the new nnU-Net!Clickhereif you were looking for the old one instead.Coming from V1? Check out theTLDR Migration Guide. Reading the rest of the documentation is still strongly recommended ;-)What is nnU-Net?Image datasets are enormously diverse: image dimensionality (2D, 3D), modalities/input channels (RGB image, CT, MRI, microscopy, ...),
image sizes, voxel sizes, class ratio, target structure properties and more change substantially between datasets.
Traditionally, given a new problem, a tailored solution needs to be manually designed and optimized  - a process that
is prone to errors, not scalable and where success is overwhelmingly determined by the skill of the experimenter. Even
for experts, this process is anything but simple: there are not only many design choices and data properties that need to
be considered, but they are also tightly interconnected, rendering reliable manual pipeline optimization all but impossible!nnU-Net is a semantic segmentation method that automatically adapts to a given dataset. It will analyze the provided
training cases and automatically configure a matching U-Net-based segmentation pipeline. No expertise required on your
end! You can simply train the models and use them for your application.Upon release, nnU-Net was evaluated on 23 datasets belonging to competitions from the biomedical domain. Despite competing
with handcrafted solutions for each respective dataset, nnU-Net's fully automated pipeline scored several first places on
open leaderboards! Since then nnU-Net has stood the test of time: it continues to be used as a baseline and method
development framework (9 out of 10 challenge winners at MICCAI 2020and 5 out of 7
in MICCAI 2021 built their methods on top of nnU-Net,we won AMOS2022 with nnU-Net)!Please cite thefollowing paperwhen using nnU-Net:Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring 
method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.What can nnU-Net do for you?If you are adomain scientist(biologist, radiologist, ...) looking to analyze your own images, nnU-Net provides
an out-of-the-box solution that is all but guaranteed to provide excellent results on your individual dataset. Simply
convert your dataset into the nnU-Net format and enjoy the power of AI - no expertise required!If you are anAI researcherdeveloping segmentation methods, nnU-Net:offers a fantastic out-of-the-box applicable baseline algorithm to compete againstcan act as a method development framework to test your contribution on a large number of datasets without having to
tune individual pipelines (for example evaluating a new loss function)provides a strong starting point for further dataset-specific optimizations. This is particularly used when competing
in segmentation challengesprovides a new perspective on the design of segmentation methods: maybe you can find better connections between
dataset properties and best-fitting segmentation pipelines?What is the scope of nnU-Net?nnU-Net is built for semantic segmentation. It can handle 2D and 3D images with arbitrary
input modalities/channels. It can understand voxel spacings, anisotropies and is robust even when classes are highly
imbalanced.nnU-Net relies on supervised learning, which means that you need to provide training cases for your application. The number of
required training cases varies heavily depending on the complexity of the segmentation problem. No
one-fits-all number can be provided here! nnU-Net does not require more training cases than other solutions - maybe
even less due to our extensive use of data augmentation.nnU-Net expects to be able to process entire images at once during preprocessing and postprocessing, so it cannot
handle enormous images. As a reference: we tested images from 40x40x40 pixels all the way up to 1500x1500x1500 in 3D
and 40x40 up to ~30000x30000 in 2D! If your RAM allows it, larger is always possible.How does nnU-Net work?Given a new dataset, nnU-Net will systematically analyze the provided training cases and create a 'dataset fingerprint'.
nnU-Net then creates several U-Net configurations for each dataset:2d: a 2D U-Net (for 2D and 3D datasets)3d_fullres: a 3D U-Net that operates on a high image resolution (for 3D datasets only)3d_lowres→3d_cascade_fullres: a 3D U-Net cascade where first a 3D U-Net operates on low resolution images and
then a second high-resolution 3D U-Net refined the predictions of the former (for 3D datasets with large image sizes only)Note that not all U-Net configurations are created for all datasets. In datasets with small image sizes, the
U-Net cascade (and with it the 3d_lowres configuration) is omitted because the patch size of the full
resolution U-Net already covers a large part of the input images.nnU-Net configures its segmentation pipelines based on a three-step recipe:Fixed parametersare not adapted. During development of nnU-Net we identified a robust configuration (that is, certain architecture and training properties) that can
simply be used all the time. This includes, for example, nnU-Net's loss function, (most of the) data augmentation strategy and learning rate.Rule-based parametersuse the dataset fingerprint to adapt certain segmentation pipeline properties by following
hard-coded heuristic rules. For example, the network topology (pooling behavior and depth of the network architecture)
are adapted to the patch size; the patch size, network topology and batch size are optimized jointly given some GPU
memory constraint.Empirical parametersare essentially trial-and-error. For example the selection of the best U-net configuration
for the given dataset (2D, 3D full resolution, 3D low resolution, 3D cascade) and the optimization of the postprocessing strategy.How to get started?Read these:Installation instructionsDataset conversionUsage instructionsAdditional information:Region-based trainingManual data splitsPretraining and finetuningIntensity Normalization in nnU-NetManually editing nnU-Net configurationsExtending nnU-NetWhat is different in V2?Competitions:AutoPET IIWhere does nnU-Net perform well and where does it not perform?nnU-Net excels in segmentation problems that need to be solved by training from scratch,
for example: research applications that feature non-standard image modalities and input channels,
challenge datasets from the biomedical domain, majority of 3D segmentation problems, etc . We have yet to find a
dataset for which nnU-Net's working principle fails!Note: On standard segmentation
problems, such as 2D RGB images in ADE20k and Cityscapes, fine-tuning a foundation model (that was pretrained on a large corpus of
similar images, e.g. Imagenet 22k, JFT-300M) will provide better performance than nnU-Net! That is simply because these
models allow much better initialization. Foundation models are not supported by nnU-Net as
they 1) are not useful for segmentation problems that deviate from the standard setting (see above mentioned
datasets), 2) would typically only support 2D architectures and 3) conflict with our core design principle of carefully adapting
the network topology for each dataset (if the topology is changed one can no longer transfer pretrained weights!)What happened to the old nnU-Net?The core of the old nnU-Net was hacked together in a short time period while participating in the Medical Segmentation
Decathlon challenge in 2018. Consequently, code structure and quality were not the best. Many features
were added later on and didn't quite fit into the nnU-Net design principles. Overall quite messy, really. And annoying to work with.nnU-Net V2 is a complete overhaul. The "delete everything and start again" kind. So everything is better
(in the author's opinion haha). While the segmentation performanceremains the same, a lot of cool stuff has been added.
It is now also much easier to use it as a development framework and to manually fine-tune its configuration to new
datasets. A big driver for the reimplementation was also the emergence ofHelmholtz Imaging,
prompting us to extend nnU-Net to more image formats and domains. Take a lookherefor some highlights.AcknowledgementsnnU-Net is developed and maintained by the Applied Computer Vision Lab (ACVL) ofHelmholtz Imagingand theDivision of Medical Image Computingat theGerman Cancer Research Center (DKFZ).