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https://github.com/PJLallen/D2GPLand
Official Implementation of MICCAI 2024 paper "Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection"
https://github.com/PJLallen/D2GPLand
Last synced: about 1 month ago
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Official Implementation of MICCAI 2024 paper "Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection"
- Host: GitHub
- URL: https://github.com/PJLallen/D2GPLand
- Owner: PJLallen
- License: mit
- Created: 2024-06-21T09:54:18.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-29T06:02:06.000Z (about 1 month ago)
- Last Synced: 2024-11-29T07:19:17.877Z (about 1 month ago)
- Language: Python
- Size: 3.01 MB
- Stars: 10
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# D2GPLand [MICCAI'24][Oral, Finalist]
Official Implementation of MICCAI-2024 Oral paper "[Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection](https://arxiv.org/abs/2406.17858)".
[Jialun Pei](https://scholar.google.com/citations?user=1lPivLsAAAAJ&hl=en), [Ruize Cui](https://scholar.google.com/citations?hl=zh-CN&user=rAcxfuUAAAAJ), Yaoqian Li, [Weixin Si](https://scholar.google.com/citations?user=E4efwTgAAAAJ&hl=zh-CN&oi=ao), [Jing Qin](https://harry-qinjing.github.io/), and [Pheng-Ann Heng](https://scholar.google.com/citations?user=OFdytjoAAAAJ&hl=zh-CN)
👀 [[Paper]](https://arxiv.org/abs/2406.17858); [[Official Version]]()
**Contact:** [email protected], [email protected]
## 🔧 Environment preparation
The code is tested on python 3.9.19, pytorch 2.0.1, and CUDA 11.7, change the versions below to your desired ones.
1. Clone repository:
```shell
git clone https://github.com/PJLallen/D2GPLand.git
cd D2GPLand
```
2. Set up anaconda environment:
```shell
# Create D2GPLand anaconda environment from YAML.file
conda env create -f D2GPLand.yaml
# Activate environment
conda activate D2GPLand
```
## 📈 Dataset preparation
### 💥 Download proposed L3D dataset
- L3D dataset: [Google Drive](https://drive.google.com/drive/folders/1jP4m7_0oP6-srTknS5NAp0Dr8gzkydrI?usp=sharing)
### Register datasets
Change the path of the datasets as:
```shell
DATASET_ROOT = 'D2GPLand/L3D/'
TRAIN_PATH = os.path.join(DATASET_ROOT, 'Train/')
TEST_PATH = os.path.join(DATASET_ROOT, 'Test/')
VAL_PATH = os.path.join(DATASET_ROOT, 'Val/')
```
## 🚀 Pre-trained weights
D2GPLand with SAM-b and ResNet-34: [Google Drive](https://drive.google.com/drive/folders/1Mll-izyMLoCnTxfW5LOJhzaThipnUSg0?usp=drive_link)
## ⚙️ Usage
### Train
```shell
python train.py --data_path {PATH_TO_DATASET} \
--batch_size 4 --lr 1e-4 --decay_lr 1e-6 --epoch 60
```
Please replace {PATH_TO_DATASET} to your own dataset dir
### Eval
```shell
python test.py --model_path {PATH_TO_THE_MODEL_WEIGHTS} \
--prototype_path {PATH_TO_THE_PROTOTYPE_WEIGHTS} \
--data_path {PATH_TO_DATASET}
```
- `{PATH_TO_THE_MODEL_WEIGHTS}`: please put the pre-trained model weights here
- `{PATH_TO_THE_PROTOTYPE_WEIGHTS}`: please put the pre-trained prototype weights here
- `{PATH_TO_DATASET}`: please put the dataset dir here
## Acknowledgement
This work is based on:
- [AdelaiDepth](https://github.com/aim-uofa/AdelaiDepth)
- [Segment Anything Model](https://github.com/facebookresearch/segment-anything)
Thanks them for their great work!
## 📚 Citation
If this helps you, please cite this work:
```bibtex
@inproceedings{pei2024land,
title={Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection},
author={Pei, Jialun and Cui, Ruize and Li, Yaoqian and Si, Weixin and Qin, Jing and Heng, Pheng-Ann},
booktitle={MICCAI},
year={2024},
organization={Springer}
}
```