  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).
