ProMISe使用预训练图像基础模型进行提示驱动的 3D 医学图像分割.zip

# advanced-ProMISe [](https://arxiv.org/pdf/2310.19721.pdf) ProMISe: **Pro**mpt-driven 3D **M**edical **I**mage **Se**gmentation Using Pretrained Image Foundation Models --------------------------------- **Recent news** (11/13/23) The [pretrained ProMISe](https://drive.google.com/drive/folders/1Yol2tIaNYVve6JQ3osg2pjDRgwVeS-IF?usp=sharing) models and [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) are uploaded. (11/12/23) The code is uploaded and updated. --------------------------------- **Datasets** Here are the [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) that we used in our experiments, which are modified based on the original datasets from [Medical Segmentation Decathlon](http://medicaldecathlon.com/). We used two public datasets, e.g. task 07 and 10 for pancreas and colon tumor segmentations, respectively. **Installation** ``` conda create -n promise python=3.9 conda activate promise (Optional): sudo install git pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 # install pytorch pip install git+https://github.com/facebookresearch/segment-anything.git # install segment anything packages pip install git+https://github.com/deepmind/surface-distance.git # for normalized surface dice (NSD) evaluation pip install -r requirements.txt ``` **Training** ``` python train.py --data colon --data_dir your_data_directory --save_dir to_save_model_and_log ``` **Test** ``` python test.py --data colon --data_dir your_data_directory --save_dir to_save_model_and_log --split test ``` use [pretrained ProMISe](https://drive.google.com/drive/folders/1Yol2tIaNYVve6JQ3osg2pjDRgwVeS-IF?usp=sharing). --use_pretrain --pretrain_path /your_downladed_path/colon_pretrain_promise.pth **Tips** - Set "num_worker" based on your cpu to boost the data loading speed, it matters. From my device, loading data takes 30 seconds if num_workers = 1. - please specify the save_name. - don't forget to download the pretrained SAM model from [SAM-B](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth), and set the path as "checkpoint_sam". - set "save_prediction" and "save_base_dir" if you want to save inference predictions. - more details can be viewed in /config/config_args.py TODO: 1. build this page for better instruction. 2. Pytorch DistributedDataParallel. The DDP implementation can be viewed in our [latest work](https://github.com/MedICL-VU/PRISM) --------------------------------- Please shoot an email to hao.li.1@vanderbilt.edu for any questions, and I am always happy to help! :) ``` @article{li2023promise, title={Promise: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models}, author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek}, journal={arXiv preprint arXiv:2310.19721}, year={2023} } ``` ``` @article{li2023assessing, title={Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts}, author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek}, journal={arXiv preprint arXiv:2311.07806}, year={2023} } ```