# Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames

arXiv: https://arxiv.org/abs/1609.01329 · HTML: https://mlsanity.com/paper/1609.01329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos](https://mlsanity.com/paper/2106.12522/) (2021)
- [CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy](https://mlsanity.com/paper/2206.14951/) (2022)
- [Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations](https://mlsanity.com/paper/2101.07280/) (2021)
- [RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches](https://mlsanity.com/paper/2310.00868/) (2023)

## Abstract (arXiv)

We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.