dynunet_pipeline

Overview

This pipeline is modified from NNUnet [1][2] which wins the "Medical Segmentation Decathlon Challenge 2018" and open sourced from https://github.com/MIC-DKFZ/nnUNet.

Data

The source decathlon datasets can be found from http://medicaldecathlon.com/.

After getting the dataset, please run create_datalist.py to get the datalists (please check the command line arguments first). The default seed can help to get the same 5 folds data splits as NNUnet has, and the created datalist will be in config/

My running environment:

• OS: Ubuntu 20.04.1 LTS
• Python: 3.8.5
• Pytorch: 1.8.0

To prevent the inconsistency, all json files are included in config/ already.

Task parameters

All task specific parameters refer from nnUNet pipeline (as shown in task_params.py). During these parameters, patch_size, batch_size and deep_supr_num are achieved from some heuristic rules, and this part is not covered in this pipeline. spacing, clip_values and normalize_values are based on the statistics of the dataset (only on training set). Here we provide calculate_task_params.py and you can try to calculate them. The command is like:

python calculate_task_params.py -task_id 09

Training

Please run train.py for training. Please modify the command line arguments according to the actual situation, such as determinism_flag for deterministic training, amp for automatic mixed precision.

Validation

Please run train.py and set the argument mode to val for validation.

Inference

Please run inference.py for inference.

Examples

All training scripts for 10 tasks are included in commands/. For instance:

• train.sh is used for training.
• finetune.sh is used for finetuning.
• val.sh is used for validation.
• infer.sh is used for inference.
• If you need to use multiple GPUs, please run scripts that contain multi_gpu.

You can take task 04's scripts for reference since for other tasks, only the training parts are included. A task folder that contains train.sh means it only needs to use 1 GPU for training, and train_multi_gpu.sh means it needs at least 2 GPUs for training.

The devices I used for training for all tasks are shown as follow:

task	number of GPUs used (Tesla V100 32GB)
1	2
2	1
3	4
4	1
5	1
6	1
7	2
8	2
9	1
10	1

I used these scripts and trained for all 5 folds for all 10 tasks. As for the test set, I did the ensemble by average the 5 feature maps (coming from 5 folds' models) before the argmax manipulation (for task 03, since the feature maps are very large, I just did voting for 5 final predictions). By submitting the ensembled results to the Decathlon Challenge's Leaderboard, I got the following results:

	DynUNet class 1	2	3	NNUNet class 1	2	3
task 01	0.68	0.47	0.69	0.68	0.47	0.68

	DynUNet class 1	NNUNet class 1
task 02	0.93	0.93
task 06	0.67	0.74
task 09	0.96	0.97
task 10	0.55	0.58

	DynUNet class 1	2	NNUNet class 1	2
task 03	0.95	0.72	0.96	0.76
task 04	0.90	0.88	0.90	0.89
task 05	0.71	0.87	0.77	0.90
task 07	0.81	0.54	0.82	0.53
task 08	0.66	0.71	0.66	0.72

Comments:

• The results of DynUNet come from the re-implemented 3D_fullres version in MONAI and without postprocessing.

• The results of NNUnet come from different versions (3D_fullres for task 01, 02 and 04, 3D_cascade for task 10, and ensembled two versions for other tasks) and may have postprocessing [1].

• Therefore, the two results may not be fully comparable and the above tables are just for reference.

• After implementing this repository, I re-trained on task 04 and attached the validation results as follow, and the comparisons between DynUNet and NNUnet are all for the single 3D_fullres version.

As for task 04, with the default settings in train.sh and finetune.sh, you can get around the following validation results:

	0	1	2	3	4	Mean	NNUNet val
class 1	0.9007	0.8930	0.8985	0.8979	0.9015	0.8983	0.8975
class 2	0.8835	0.8774	0.8826	0.8818	0.8828	0.8816	0.8807

References

[1] Isensee F, Jäger P F, Kohl S A A, et al. Automated design of deep learning methods for biomedical image segmentation[J]. arXiv preprint arXiv:1904.08128, 2019.

[2] Isensee F, Petersen J, Klein A, et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation[J]. arXiv preprint arXiv:1809.10486, 2018.