Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Last update: Jan 03, 2023

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Deep Learning SASSnet

Overview

SASSnet

Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020)

Our code is origin from UA-MT

You can find paper in Arxiv.

Usage

Clone the repo:

git clone https://github.com/kleinzcy/SASSnet.git 
cd SASSnet

Put the data in data/2018LA_Seg_Training Set.
Train the model

cd code
# for 16 label
python train_gan_sdfloss.py --gpu 0 --label 16 --consistency 0.01 --exp model_name
# for 8 label
python train_gan_sdfloss.py --gpu 0 --label 8 --consistency 0.015 --exp model_name

Params are the best setting in our experiment.

Test the model

python test_LA.py --model model_name --gpu 0 --iter 6000

Our best model are saved in model dir.

Citation

If you find our work is useful for you, please cite us.

Owner

klein

2016-2020:Undergraduate student in WHU. 2020-now:Graduate student in Shanghai tech university, websit http://plus.sist.shanghaitech.edu.cn/

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Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Related tags

Overview

SASSnet

Usage

Citation

Owner

klein

[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

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