mlsanityData to 7 Oct 2026

Estimating the coverage in 3d reconstructions of the colon from colonoscopy videos

arXiv 2210.10459 ↗First posted 19 Oct 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Colonoscopy is the most common procedure for early detection and removal of polyps, a critical component of colorectal cancer prevention. Insufficient visual coverage of the colon surface during the procedure often results in missed polyps. To mitigate this issue, reconstructing the 3D surfaces of the colon in order to visualize the missing regions has been proposed. However, robustly estimating the local and global coverage from such a reconstruction has not been thoroughly investigated until now. In this work, we present a new method to estimate the coverage from a reconstructed colon pointcloud. Our method splits a reconstructed colon into segments and estimates the coverage of each segment by estimating the area of the missing surfaces. We achieve a mean absolute coverage error of 3-6% on colon segments generated from synthetic colonoscopy data and real colonography CT scans. In addition, we show good qualitative results on colon segments reconstructed from real colonoscopy videos.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 19 Oct 2022.

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9 of 27 references.

References · 9

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  3. 2016Direct Sparse Odometrycited by 479
  4. 2018PCN: Point Completion Networkcited by 382
  5. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  6. 2020Point Cloud Completion by Skip-attention Network with Hierarchical Foldingcited by 106
  7. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
  8. 2020Detecting Deficient Coverage in Colonoscopiescited by 14
  9. 2022C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopycited by 7

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