LUCID:

Usage

step 1: installation

git clone git@github.com:YixinChen-AI/LUCID.git
cd LUCID
chmod 777 ./install.sh
./install.sh

还需要根据自己的CUDA版本安装torch，torch official website: https://pytorch.org/

step 2: well-trained ckpt

将下载好的模型权重放到./model_weight/目录下.

目前权重文件需要联系作者YChen,邮箱是2311110791@stu.pku.edu.cn

step 3: inference

1. 可以在python中调用

from lucid_utils_low import lucid
lucid("./testdata/autopet/CTres.nii.gz", # input ct nii.gz path **required**
      output_seg_path="./testdata/totalseg/output123.nii.gz", ## segmentation prediction result **required**
      output_stdct_path=None, # the input ct will be resampled and re-oriented in LUCIDA protocol and the processed CT image will be saved in this path **optional**
      modelname="STUNet_large",
      modelweight="./model_weight/lucid_STUNet_large_192e40.pth",
     output=112)

2. 可以在命令行中调用 (暂未更新使用，暂时废弃)

python lucid.py --ct ct_nii_path --gpu 0

update

1. v0.0. 2023/12/16.
   1. lucid_STUNet_large_192e65.pth, lucid_swinunetr_192e69, lucid_unet_large_192e65.pth.
   2. half inference. need 12GB GPU.
2. v0.1. 2023/12/28. duct_STUNet_large_192e05.pth.
   1. Issue: v0.0 cost too much CPU if input is too huge. Generaly for a whole-body CT image with 1.5mm spacing, the input could be larger than 700x300x300 and the inference proecss contain more than 100 192x192x192 patches. v0.0 need 2.2GB memory for each patches so for a large CT image, server need more than 128+ GB memory. v0.1 remove sigmoid, which is useless in the argmax (1GB each patches); half inference.