# Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset

arXiv: https://arxiv.org/abs/2210.14771 · HTML: https://mlsanity.com/paper/2210.14771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Robust Medical Instrument Segmentation Challenge 2019](https://mlsanity.com/paper/2003.10299/) (2020)
- [Robotic Endoscope Control via Autonomous Instrument Tracking](https://mlsanity.com/paper/2107.02317/) (2021)
- [Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction](https://mlsanity.com/paper/2109.15098/) (2021)

## Built on by (most cited first, 5 of 5)

- [Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency](https://mlsanity.com/paper/2403.06683/) (2024)
- [ROBUST-MIPS: A Combined Skeletal Pose and Instance Segmentation Dataset for Laparoscopic Surgical Instruments](https://mlsanity.com/paper/2508.21096/) (2025)
- [Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation](https://mlsanity.com/paper/2506.21150/) (2025)
- [Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing](https://mlsanity.com/paper/2307.11638/) (2023)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)

## Abstract (arXiv)

Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging. The lack of rigorous investigation into the topic combined with the lack of a common benchmark dataset for this task has been a long-lasting issue in the field. In this paper, we propose two variants of a lean GPU-based computational pipeline combining edge detection and circle fitting. The two variants differ by relying on handcrafted features, and learned features respectively to extract content area edge point candidates. We also present a first-of-its-kind dataset of manually annotated and pseudo-labelled content areas across a range of surgical indications. To encourage further developments, the curated dataset, and an implementation of both algorithms, has been made public (https://doi.org/10.7303/syn32148000, https://github.com/charliebudd/torch-content-area). We compare our proposed algorithm with a state-of-the-art U-Net-based approach and demonstrate significant improvement in terms of both accuracy (Hausdorff distance: 6.3 px versus 118.1 px) and computational time (Average runtime per frame: 0.13 ms versus 11.2 ms).