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%≈95%) with significant efficiency (10∼40×10∼40× faster than prior arts).
The RibSeg Dataset contains annotations for both rib segmentation and centerline.
Over view of RibSeg dataset:
| Subset | No. of CT Scans | No. of Individual Ribs |
|---|---|---|
| Training | 320 | 7,670 |
| Development | 50 | 1,187 |
| Test | 120 | 2,862 |
For training data, please download the source CT scans from RibFrac Dataset to ./data/ribfrac directory:
training set part1: https://zenodo.org/record/3893508#.YUtisbj0kac
training set part2: https://zenodo.org/record/3893498#.YUti2bj0kac
test set: https://zenodo.org/record/3993380#.YUti67j0kac
validation set: https://zenodo.org/record/3893496#.YUtjCLj0kac
For annotations, download RibSeg dataset to ./data/ribseg directory
please refer to https://zenodo.org/record/5336592#.YUtkIbj0kac
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++.
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