Awesome
MemSAM
MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation, CVPR 2024, Oral
Xiaolong Deng^, Huisi Wu*, Runhao Zeng, Jing Qin
<!-- ![MemSAM Design](/assets/framework.jpg) --> <div align=center> <img src="/assets/framework.jpg" width="600" alt="MemSAM Design" /> </div> <!-- The code will be uploaded later. -->Installation
conda create --name memsam python=3.10
conda activate memsam
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install requirements.txt
Usage
prepare dataset
First, download the dataset from:
Then process the dataset according to utils/preprocess_echonet.py
and utils/preprocess_camus.py
, for example:
# CAMUS
python utils/preprocess_camus.py -i /data/dengxiaolong/CAMUS_public/database_nifti -o /data/dengxiaolong/memsam/CAMUS_public
# EchoNet-Dynamic
python utils/preprocess_echonet.py -i /data/dengxiaolong/EchoNet-Dynamic -o /data/dengxiaolong/memsam/EchoNet
pretrain checkpoint download
train and test
Use train_video.py
and test_video.py
to train and test separately.
Acknowledgement
The work is based on SAM, SAMUS and XMem. Thanks for the open source contributions to these efforts!
Citation
if you find our work useful, please cite our paper, thank you!
@InProceedings{Deng_2024_CVPR,
author = {Deng, Xiaolong and Wu, Huisi and Zeng, Runhao and Qin, Jing},
title = {MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {9622-9631}
}