Name
Abstract | ||
|---|---|---|
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 approximate to 95%) with significant efficiency (10-40x faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at https://github.com/M3DV/RibSeg. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87193-2_58 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I |
Keywords | DocType | Volume |
Rib segmentation, Rib centerline, Medical image dataset, Point clouds, Computed tomography | Conference | 12901 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiancheng Yang | 1 | 20 | 6.74 |
| Shixuan Gu | 2 | 0 | 0.34 |
| Donglai Wei | 3 | 0 | 1.01 |
| Hanspeter Pfister | 4 | 5933 | 340.59 |
| Bingbing Ni | 5 | 1421 | 82.90 |