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Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes

Implementation for TMI 2024 paper Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes by Shizhan Gong, Yonghao Long, Kai Chen, Jiaqi Liu, Yuliang Xiao, Alexis Cheng, Zerui Wang, and Qi Dou.

Sample Results

supplementary.mp4

Setup

We recommend to set up the environment with the following command.

pip install -r requirements.txt

We managed to test our code on Ubuntu 18.04 with Python 3.8 and CUDA 11.3. Our implementation is based on single GPU setting.

Dataset

We provide a sample clip in the folder sample_data. Please arrange your own data as follows

folder/
	└── clip1/ 
	    └── img_left/
	        └── 0001.jpg
	        └── 0002.jpg
	        └── ...
	    └── img_right/
	        └── 0001.jpg
	        └── 0002.jpg
	        └── ...
	└── clip2/
	    └── img_left/
	    └── img_right/ 
	└── ...

where each clip folder corresponds to a short video clip, img_left are the left view of the stereo images, and img_right are the right view of the stereo images. Each image with img_left and img_right represents a single frame.

Pre-processing

Depth Estimation

The code for Depth estimation is adapted from stereo-transformer. First go to the data_preprocessing/depth_estimation folder and install the required environment.

cd data_preprocessing/depth_estimation
pip install -r requirements.txt

Then type the command below for generating disparity maps

python main.py --address path/to/store/data --model_file_name path/to/pretrained-checkpoint

--address denote the path of the datafolder and --model_file_name denote the path of pretrained checkpoint. The checkpoint can be downloaded here.

Instrument Segmentation

First go to the data_preprocessing/tool_segmentation folder and install the required environment.

cd data_preprocessing/tool_segmentation
conda env create -f environment.yml
conda activate CsrSeg

Then type the command below for generating tool segmentation masks.

python main.py --address path/to/store/data --model_path path/to/pretrained-checkpoint

--address denote the path of the datafolder and --model_path denote the path of pretrained checkpoint. Our pretrained checkpoint can be downloaded here.

Training

Type the command below to for training the model.

python main.py --train_data train.pkl --eval_data eval.pkl --output_dir path/to/store/checkpoint

--train_data and --eval_data stores the meta information of the training and validation data. --output_dir stores the trained checkpoint.

Here is an example of train.pkl:

[{'path': 'path/to/data/folder/clip1/',
  't0': '0001',
  't1': '0002',
  't2': '0003',
  't3': '0004',
  't4': '0005'},
 {'path': 'path/to/data/folder/clip1/',
  't0': '0002',
  't1': '0003',
  't2': '0004',
  't3': '0005',
  't4': '0006'},
  ...]

and another example of eval.pkl:

[{'path': 'path/to/data/folder/clip1/',
  'sequence': ['0001',
   '0002',
   '0003',
   '0004',
   '0005',
   '0006',
   '0007',
   '0008',
   ...],},
   ...]

Each sequence in --eval_data corresponds to only 5 frame while each sequence in eval corresponds to a longer clip.

Inference

Type the command below to for model inference.

python main.py --test_data test.pkl --model_path path/to/pretrained-checkpoint

--test_data stores the meta information of the test data. --model_path denote the path of pretrained checkpoint. Our pretrained checkpoint can be downloaded here. test.pkl has the same format as eval.pkl.

Contact

For any questions, please contact [email protected]