RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

• URL: http://arxiv.org/abs/2109.09521v1
• Date: Fri, 17 Sep 2021 16:17:35 GMT
• Title: RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
• Authors: Jiancheng Yang, Shixuan Gu, Donglai Wei, Hanspeter Pfister, Bingbing Ni
• Abstract summary: Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive. We develop a labeled rib segmentation benchmark, named emphRibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. We thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation.
• Score: 62.16198969529679
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \emph{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~$\approx95\%$) with significant efficiency ($10\sim40\times$ faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at https://github.com/M3DV/RibSeg.

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