This repo is the official implementation of Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation which is accepted at NeurIPS-2023.
🚀 The significance of this work lies in its ability to encourage semi-supervised medical image segmentation methods to address more complex real-world application scenarios, rather than just developing frameworks in ideal experimental environments. Furthermore, we have consolidated all four settings within this single codebase, enabling the execution of any task using a single bash file by merely adjusting the arguments.
Online Presentation Video is available for brief introduction.
First, create a new environment and install the requirements:
conda create -n genericssl python=3.8
conda activate genericssl
cd GenericSSL/
pip install -r requirements.txt[📌IMPORTANT] Then, before running the code, set the PYTHONPATH to pwd:
export PYTHONPATH=$(pwd)/code:$PYTHONPATHFirst, download the datasets and put them under the Datasets folder:
-
LASeg dataset for SSL: download the preprocessed data from https://github.com/yulequan/UA-MT/tree/master/data.
-
Synapse dataset for IBSSL: The MR imaging scans are available at https://www.synapse.org/#!Synapse:syn3193805/wiki/. Please sign up and download the dataset. 🚀🚀🚀 Or download the preprossed data via this link.
-
MMWHS dataset for UDA: download according to https://github.com/cchen-cc/SIFA#readme. 🚀🚀🚀 Or download the preprossed data via this link.
-
M&Ms dataset for SemiDG: download from https://www.ub.edu/mnms/, after unzipping the dataset, you will get an
OpenDatasetfolder, then you need to run thepython coda/data/split_MNMS_data.pyto spilt and getMNMsfolder. 🚀🚀🚀 Or download the preprossed data via this link.
The file structure should be:
.
├── Datasets
│ ├── LASeg
│ │ ├── 2018LA_Seg_Training Set
│ │ │ ├── 0RZDK210BSMWAA6467LU
│ │ │ │ ├── mri_norm2.h5
│ │ │ ├── 1D7CUD1955YZPGK8XHJX
│ │ │ └── ...
│ │ ├── test.list
│ │ └── train.list
│ ├── MMWHS
│ │ ├── CT
│ │ │ ├── imagesTr
│ │ │ │ ├── ct_train_1001_image.nii.gz
│ │ │ │ └── ...
│ │ │ └── labelsTr
│ │ │ │ ├── ct_train_1001_label.nii.gz
│ │ │ │ └── ...
│ │ └── MR
│ │ ├── imagesTr
│ │ └── labelsTr
│ ├── MNMs
│ │ └── Labeled
│ │ ├── VendorA
│ │ │ ├── A0S9V9
│ │ │ │ ├── A0S9V9_sa.nii.gz
│ │ │ │ ├── A0S9V9_sa_gt.nii.gz
│ │ │ ├── A1D9Z7
│ │ │ └── ...
│ │ ├── VendorB
│ │ ├── VendorC
│ │ └── VendorD
│ ├── OpenDataset
│ │ ├── Testing
│ │ ├── Training
│ │ ├── Validation
│ │ └── mnms_dataset_info.xls
│ └── Synapse
│ ├── imagesTr
│ │ ├──img0001.nii.gz
│ │ └── ...
│ └── labelsTr
│ ├──label0001.nii.gz
│ └── ...Run python ./code/data/preprocess_la.py to:
- convert
.h5files to.npy. - generate the labeled/unlabeled splits
Run python ./code/data/preprocess_synapse.py to
- resize the images and convert to
.npyfor faster loading; - generate the train/test splits (use labeled data for validation);
- generate the labeled/unlabeled splits.
Run python ./code/data/preprocess_mmwhs.py to:
- reorient to the same orientation, RAI;
- convert to continuous labels;
- crop centering at the heart region;
- for each 3D cropped image top 2/% of its intensity histogram was cut off for alleviating artifacts;
- resize and convert to
.npy; - generate the train/validation/test splits.
Run python ./code/data/preprocess_mnms.py to:
- split the original 4D data to 3D along the time axis;
- crop and resize;
- save to
.npy; - generate the train/test splits (use labeled data for validation);
- generate the labeled/unlabeled splits.
For all the pre-processing, you can comment out the functions corresponding to splits and use our pre-split files.
Finally, you will get a file structure as follow:
.
├── Synapse_data
│ ├── npy
│ │ ├── <id>_image.npy
│ │ ├── <id>_label.npy
│ │ └── ...
│ └── split_txts
│ ├── labeled_0.2.txt
│ ├── unlabeled_0.2.txt
│ ├── train.txt
│ ├── eval.txt
│ ├── test.txt
│ └── ...
├── LA_data
│ └── ...
├── MMWHS_data
│ └── ...
└── MNMS_data
└── ...🔥🔥 This codebase allows train, test, and evaluate on all the four settings using one single bash file. 🔥🔥
Run the following commands for training, testing and evaluating.
bash train.sh -c 0 -e diffusion -t <task> -i '' -l 1e-2 -w 10 -n 300 -d true Parameters:
-c: use which gpu to train
-e: use which training script, can be diffusion for train_diffusion.py, or diffusion_2d for train_diffusion_2d.py
-t: switch to different tasks:
For SSL on 5% labeled LA dataset: la_0.05
For IBSSL on 20% labeled Synapse dataset: synapse_0.2
For UDA on MMWHS dataset: mmwhs_ct2mr for labeled CT and unlabeled MR, mmwhs_mr2ct in opposite
For SemiDG on M&Ms dataset, 2% labeled B,C,D -> A setting: mnms_A_0.02; 5% labeled A,B,C -> D setting: mnms_D_0.05
-i: name of current experiment, can be whatever you like
-l: learning rate
-w: weight of unsupervised loss
-n: max epochs
-d: whether to train, if true, training -> testing -> evaluating; if false, testing -> evaluating
🌟🌟 All trained model weights can be downloaded from this link. 🌟🌟
Put the logs directory under the root directory of this repo and set -d False, then you can test and evaluate the models.
If this code is helpful for your study, please cite:
@inproceedings{wang2023towards,
title={Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation},
author={Wang, Haonan and Li, Xiaomeng},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}
Haonan Wang (hwanggr@connect.ust.hk)
This repository is released under MIT License.