EndoL2H: Deep Super-Resolution for Capsule Endoscopy
This work addresses the limitation of poor image resolution in capsule endoscopy, which could improve diagnosis and automated detection of diseases like polyps, though it appears incremental as it builds on existing super-resolution techniques.
The authors tackled the problem of low-resolution images in wireless capsule endoscopy by proposing a deep learning framework that enhances resolution by up to 12x, outperforming state-of-the-art methods in quantitative and qualitative evaluations.
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative studies performed demonstrate the superiority of EndoL2H over state-of-the-art deep super-resolution methods DBPN, RCAN and SRGAN. MOS tests performed by 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https://github.com/CapsuleEndoscope/EndoL2H.