RibSeg

Please see the RibSeg v2 paper on IEEE TMI 2023.

For code of RibSeg v2, please refer to the branch ribsegv2.

For code of RibSeg v1, please refer to the branch ribsegv1.

Released! Welcome to use and leave comments! Please cite RibSeg v2!

RibSeg v2 dataset, description document, and annotations as mesh here.

To load the data:

seg: import nibabel as nib seg = nib.load(file name).get_fdata() # seg is a np array / volume of (512,512,N) with rib labels

cl: import numpy as np cl = np.load(file name)['cl'] # cl is a np array of (24,500,3), each rib contains 500 points

Paper (MICCAI'21) | Dataset (Zenodo)

Jiancheng Yang, Shixuan Gu, Donglai Wei, Hanspeter Pfister, Bingbing Ni

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named RibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves state-of-the-art segmentation performance (Dice≈95%) with significant efficiency (10∼40× faster than prior arts).

Dataset

The RibSeg Dataset contains annotations for both rib segmentation and centerline.

Rib Segmentation	Rib Centerline

Overview of RibSeg dataset:

Subset	No. of CT Scans	No. of Individual Ribs
Training	320	7,670
Development	50	1,187
Test	120	2,862

Model Training (RibSeg v1)

For training data, please download the source CT scans from RibFrac Dataset to ./data/ribfrac directory:

Download

For the source CT scans, please refer to the MICCAI 2020 RibFrac Challenge on grand-challenge.org (click Join first).

For annotations, please download RibSeg dataset to ./data/ribseg directory on Zenodo.

Data Preparation

run data_prepare.py to create data for training.

Based on RibFrac dataset and RibSeg dataset, we binarized the CT scans and the annotations for rib segmentation to ./data/pn for the convenience of training PointNet++.

Model

You can train your model through the following command line:

python train_ribseg.py --model pointnet2_part_seg_msg --log_dir <model_directory>

You can test your model through the following command line:

python test_ribseg.py --log_dir <model_directory>

You can conduct inference through the following command line:

python inference.py --log_dir <model_directory>

You can run our model through the following command line:

python inference.py --log_dir c2_a

You can get the volume-wise test result through the following command line:

python post_proc.py

Citation

If you find this project useful, please cite our paper as:

Liang Jin, Shixuan Gu, Donglai Wei, Jason Ken Adhinarta, Kaiming Kuang, Yongjie Jessica Zhang, Hanspeter Pfister, Bingbing Ni, Jiancheng Yang, Ming Li. "RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction". IEEE Transactions on Medical Imaging (TMI), 2023.

Jiancheng Yang, Shixuan Gu, Donglai Wei, Hanspeter Pfister, Bingbing Ni. "RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans". International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021.

or using bibtex:

@article{ribsegv2,
  title={RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction},
  author={Jin, Liang and Gu, Shixuan and Wei, Donglai and Adhinarta, Jason Ken and Kuang, Kaiming and Zhang, Yongjie Jessica and Pfister, Hanspeter and Ni, Bingbing and Yang, Jiancheng and Li, Ming},
  journal={IEEE Transactions on Medical Imaging (TMI)},
  year={2023},
  publisher={IEEE}
}

@inproceedings{ribsegv1,
  title={RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans},
  author={Yang, Jiancheng and Gu, Shixuan and Wei, Donglai and Pfister, Hanspeter and Ni, Bingbing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  pages={611--621},
  year={2021},
  organization={Springer}
}