- We will update the repository as soon as possible
This is the official code of Prior Guided 3D Medical Image Landmark Localization
In this study, we present a prior guided coarse-to-fine framework for efficient and accurate 3D medical landmark detection; We utilize prior knowledge that in specific settings, physicians annotate multiple landmarks on the same slice.
The coarse stage uses coordinate regression on downsampled 3D images to maintain the structural relationships across different landmarks.
The fine stage categorizes landmarks as independent and correlated landmarks based on their annotation prior. For independent landmarks, we train multiple models to capture local features and ensure reliable local predictions. For correlated landmarks, we mimic the manual annotation process and propose a correlated landmark detection model that merges information from various patches to query key slices and identify correlated landmarks.
Our method is extensively evaluated on two datasets, exhibiting superior performance with an average detection error of 3.29 mm and 2.13 mm, respectively.
- Install PyTorch 1.13 following the official instructions
- Install dependencies
pip install -r requirements.txt
- Clone the project
git clone https://github.com/pang-yi-jie/MIDL2023_landmark.git
- You need to download the annotations files and images from PDDCA
Your data
directory should look like this:
-- data
|-- PDDCA
| |--images
| | |-- head1.nii
| | |-- head2.nii
| |-- setup
| | |-- landmark.csv
| | |-- cv0
| | |-- cv1
| | |-- cv2
|--prostate
| |--images
| | |--prostate1.nii
| | |--prostate2.nii
| |-- setup
| | |-- landmark.csv
Please download our three-fold cross-validation division.
python tools/train_PCCDA.py
# example:
python tools/train_prostate.py
python tools/test_PCCDA.py
# example:
python tools/test_prostate.py
If you find this work or code is helpful in your research, please cite:
@inproceedings{cheng2023prior,
title={Prior Guided 3D Medical Image Landmark Localization},
author={Cheng, Pujin and Lyu, Junyan and Tang, Xiaoying and others},
booktitle={Medical Imaging with Deep Learning},
year={2023}
}