
TIPs:
An accurate and automated AI tool called TIPs for Tooth Instances and Pulp segmentation from CBCT.
TIPs works out-of-the-box without requiring any retraining. By inputting a CBCT image, users can obtain both semantic and instance segmentation for teeth and pulps. The final instance labeling follows the FDI World Dental Federation notation.
Requirements: Ubuntu 20.04
, CUDA 11.8
- Create a virtual environment:
conda create -n tips python=3.10 -y
andconda activate tips
- Install Pytorch 2.0.1:
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118
- Install Mamba:
pip install causal-conv1d>=1.2.0
andpip install mamba-ssm --no-cache-dir
- Download code:
git clone https://github.com/TaoZhong11/TIPs
cd TIPs
and runpip install -e .
sanity test: Enter python command-line interface and run
import torch
import mamba_ssm
https://drive.google.com/file/d/1UuFgZ-kwRryPC-vK7w64xX0VO4iOAeGt/view?usp=drive_link
vi ~/.bashrc
export nnUNet_raw = "/home/path/to/TIPs/nnUNet_raw" # raw_data_path
export nnUNet_preprocessed = "/home/path/to/TIPs/nnUNet_preprocessed" # preprocessed_data_path
export nnUNet_results = "/home/path/to/TIPs/nnResults" # models_path
source ~/.bashrc
python TIPs.py folder_to_be_processed
The training code and data will be made available following the acceptance of the paper. Thank you!
@article{TIPs,
title={TIPs: Tooth Instances and Pulp segmentation based on hierarchical extraction and fusion of anatomical priors from cone-beam CT},
author={Tao Zhong, Yang Ning, Xueyang Wu, Li Ye, Chichi Li, Yu Du, and Yu Zhang},
journal={Under Review},
year={2024}
}
Coming soon.
We acknowledge all the authors of the employed public datasets, allowing the community to use these valuable resources for research purposes. We also thank the authors of nnU-Net, U-Mamba and Mamba for making their valuable code publicly available.