PS-Seg

The official code for PS-Seg, accoding to the following paper:

• Meng Han, Xiaochuan Ma, Xiangde Luo, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang, PS-seg: Learning from partial scribbles for 3D multiple abdominal organ segmentation. Neurocomputing, 672, April (2026): 132837.

BibTeX entry:

@article{han2026ps_seg,
author = {Meng Han and Xiaochuan Ma and Xiangde Luo and Wenjun Liao and Shichuan Zhang and Shaoting Zhang and  Guotai Wang},
title = {{PS-seg: Learning from partial scribbles for 3D multiple abdominal organ segmentation}},
year = {2026},
url = {https://doi.org/10.1016/j.neucom.2026.132837},
journal = {Neurocomputing},
volume = {672},
pages = {132837},
}

Overall Framework

The overall framework of PS-Seg：

Dataset

• The WORD dataset can be downloaded from WORD.
• The Synapse dataset can be downloaded from Synapase

Usage with PyMIC

To facilitate the use of code and make it easier to compare with other methods, we have implemented PS-Seg in PyMIC, a Pytorch-based framework for annotation-efficient segmentation. The core modules of PS-Seg in PyMIC can be found here. It is suggested to use PyMIC for this experiment. In the following, we take the WORD dataset as an example for scribble-supervised segmentation.

Step 0: Preparation

0.1. Environment Setup.

conda create -n PSSeg python=3.10
conda activate PSSeg
pip install -r requirements.txt
pip install pymic

0.2. Dataset processing.

Preprocess WORD dataset by:

python data/preprocess_WORD.py

Generate scribble label by:

python data/scribble_generator.py

To speed up the training process, we convert the data into h5 files by:

python data/image2h5.py

Step 1: Training

The configurations including dataset, network, optimizer and hyper-parameters are contained in the configure file config/psseg_word.cfg. PS-Seg needs a multi-decoder network, and it is defined in networks/TDNet_3D.py. A reimplementatin of this network has also been provided in PyMIC.

Train the PS-Seg model by running:

python run.py train config/psseg_word.cfg

Step 2: Test

Obtain predictions for testing images:

python run.py test config/psseg_word.cfg

Step 3: Compare with other weakly supervised segmentation methods

PyMIC also provides implementation of several other weakly supervised methods (learning from scribbles). Please see PyMIC_examples/seg_weak_sup/ACDC for examples.

Acknowledgement

The code of scribble-supervised learning framework is borrowed from WSL4MIS