A presentation on the "no new UNet" model, which attempts to automate hyper-parameter selection for medical image segmentation. The paper was accepted to Nature Methods.

1. NNUNET
A self-configuring method for deep learning-based
biomedical image segmentation

2. Introduction
Medical Image Segmentation is difficult
because of the vast diversity of modalities,
each of which requires a specialized pipeline
for data pre-processing and training.
nnUNet (“no new U-Net”) seeks to establish
a standardized pipeline for the medical image
segmentation process.
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3. Problem Statement
• Analysis of challenge leaderboard entries shows
that using superficially similar methods and model
architectures can lead to vastly different results
due to implementation details.
• Moreover, no single method stands out as being
necessary for high scores (except deep learning).

4. Method
• nnUNet divides hyper-parameters into 3
types:
(1) Fixed configurations
(2) Rule-Based configurations
(3) Empirical Configurations
• In all cases, validation set performance does
not affect training time, unlike in AutoML.

5. Fixed Configurations
• Model architecture (U-Net), hence the name “no new U-Net”.
• Learning rate value and scheduling (0.01 with poly decay).
• Optimizer (SGD with Nesterov momentum 0.9).
• Training procedure (250k iterations with 5-fold cross-validation and fore-ground over-sampling).
• Inference procedure (sliding window with Gaussian importance weighting).
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6. Rule-Based Configurations
• Image Intensity Normalization (Use HU if CT, else use z normalization).
• Image Resampling Strategy (If anisotropic, use cubic spline if ratio is within 3, else use nearest
neighbor interpolation).
• Image Spacing (lowest 10th percentile if anisotropic, else median).
• Use 3D cascade (if image is too large).
• Model pooling depth (reduce anisotropic side until less than 3, pool until side length becomes 4).
• Mini-batch size (largest mini-batch that fits within 11 GB during training).
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7. Rule-Based Model Configuration
• Network Topology, Patch Size, and Batch Size are configured at the start of training for effective
training within 11GB of memory.
• Networks are expected to give approximate GPU memory usage expectations, which makes
implementing new models somewhat cumbersome.
• Patch size is given high priority as large patch size is necessary for segmentation.
• Network topology is designed to pool until side length is 4 and anisotropic degree is within 3.
• Batch size is set to be lower than 5% of total data. Usually set to 1 or 2 as it has lowest priority.
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8. Empirical Parameters
• Only 2 parameters are set empirically, both of which are post-processing steps and therefore do not
affect training time.
• Suppression of non-largest segmented organ (uses the prior that humans have one or two of each
organ).
• Ensemble selection of 2D, 3D, and 3D cascade trained networks. These are selected from cross-
validation results.
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9. Comments
• Training time and memory requirements do not depend on performance on validation metrics.
• No pre-trained networks are necessary as inputs.
• No 2.5D, which may be effective in some anisotropic tasks. More robust to anisotropic data while
not abandoning the information from the anisotropic direction.
• No analysis on the effect of label quality. For example, what is the effect of nnUNet when
comparing clean and noisy labels. Is it robust to noisy labels? What attributes are important for
learning with sparsely labeled data.
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10. Results
• First place in 33 of 53 challenges with
no modifications.
• Maintains a high rank in all challenges
submitted, though in some cases,
modifications to the original nnUNet
was necessary (e.g., 2020 COVID19
segmentation challenge).
• nnUNet is the baseline for most new
medical segmentation challenges.

11. Results
• Results for COVID19 segmentation
challenge in 2020.
• nnUNet features in the top-tier of
nearly all medical segmentation
challenges.
• First place used nnUNet to pseudo-
label more data for additional
training with nnUNet.
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12. Code Analysis:
Dataset Conversion
First convert the data into nifti file
format for unified reading.
Files must be structured in the specified
format for proper training.
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13. Code Analysis: Training & Inference
• Dataset experiment planning and pre-processing:
nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity
• Model training:
nnUNet_train CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD --npz (additional options)
• Find best configuration:
nnUNet_find_best_configuration -m 2d 3d_fullres 3d_lowres 3d_cascade_fullres -t XXX --strict
• Run inference:
nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m CONFIGURATION --save_npz
• Run ensemble:
nnUNet_ensemble -f FOLDER1 FOLDER2 ... -o OUTPUT_FOLDER -pp POSTPROCESSING_FILE
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14. Resources
• Paper: https://www.nature.com/articles/s41592-020-01008-z (also read
the supplementary material)
• YouTube videos:
https://www.youtube.com/watch?v=3po8qVzz5Tc
https://www.youtube.com/watch?v=C6tpnJRpt90
• GitHub: https://github.com/MIC-DKFZ/nnUNet
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15. Related Work: nnDetection
• Paper: https://arxiv.org/abs/2106.00817
• GitHub: https://github.com/MIC-DKFZ/nnDetection
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THE END.