For hollow organs such as the oesophagus, colon and stomach, endoscopy is used to detect, monitor and treat diseases including cancer. However, endoscopy is not without its challenges and this can lead to the subtle changes in appearance caused by pre-cancerous lesions and early stage cancers to be missed. This is particularly important since cancers detected at an earlier stage have a much higher chance of cure. In an effort to overcome these challenges, computer-guided software that aids endoscopic analysis is being developed. However, progress is impeded by the presence of imaging artefacts in endoscopic videos. Indeed, frequently nearly 70% of an endoscopy video sequence1 can be corrupted by artefacts such as blurring, debris, bubbles and light reflection. It is therefore important for automatic accurate artefact detection methodology to be developed.
Sharib Ali and Felix Zhou from Jens Rittscher’s lab adopted a crowd-sourcing approach to tackle this problem by initiating the Endoscopy Artefact Detection Challenge (EAD2019). The researchers made available a collection of endoscopic images of multiple organs from diverse populations, taken using a range of endoscopy equipment and imaging modalities, and participants built algorithms to detect artefacts in this dataset. Published in Scientific Reports, the submitted algorithms were objectively compared and the ongoing challenges to developers and researchers were highlighted.
- Ali, S. et al. (2019) A deep learning framework for quality assessment and restoration in video endoscopy arXiv