mlsanityData to 7 Oct 2026

FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

arXiv 2106.12522 ↗First posted 23 Jun 2021eess.IV cs.CVcited by 5

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Abstract (arXiv)

Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. All code and scripts to reproduce the experiments of this paper will be made available via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

13 of 20 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  4. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
  5. 2019TraVeLGAN: Image-to-image Translation by Transformation Vector Learningcited by 35
  6. 2020Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translationcited by 29
  7. 2019SLAM Endoscopy enhanced by adversarial depth predictioncited by 26
  8. 2020Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessmentcited by 23
  9. 2020VR-Caps: A Virtual Environment for Capsule Endoscopycited by 23
  10. 2020Detecting Deficient Coverage in Colonoscopiescited by 14
  11. 2016Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Framescited by 4
  12. 2018Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modelingcited by 2
1 more
  1. 2021Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representationscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 2.

5 papers.

  1. 2024SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy datacited by 6
  2. 2022CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopycited by 4
  3. 2025ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priorscited by 3
  4. 2025Sim2Real in endoscopy segmentation with a novel structure aware image translationcited by 1
  5. 2023RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approachescited by 0