Abstract
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.
Highlights
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy
Artificial intelligence technology based on deep learning is being applied in various medical fields, and research is being actively conducted to develop computer-aided detection (CADe) systems for colonoscopies to overcome the limitation of the variance of human s kills[10,11,12]
The Sessile serrated lesions (SSLs) in the synthesized images depicted the real endoscopic features of SSLs, including sessile or flat morphology, a pale color, disrupted vascular pattern, altered fold contour, indistinct borders with mucus capping, and a rim of bubbles or debris (Fig. 1b). Those features and the quality of synthesized images were identified in the assessment by clinical experts
Summary
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. A meta-analysis that included five randomized control trials reported that CADe groups exhibited a 44% increase (36.6% vs 25.2%) in the adenoma detection rate (ADR) and a 70% increase (58% vs 36%) in the number of ADs per colonoscopy (APCs) when compared with the control groups[15] These well-trained CADe systems demonstrated high performance for adenoma detection[15,16,17]. Prevalence studies indicate that ADs are approximately eight times more prevalent than SSLs, and each type of polyp exhibits unique endoscopic features[29] These data imbalance problems can introduce a bias in the training process, thereby decreasing the performance of the CADe system[30]. Studies have employed GANs to generate endoscopic images that include polyps by synthesizing a normal mucosa background and a lesion patch[37,38] They showed improved detection performance on gastric cancers and colorectal polyps. A polyp detection system contributing to the detection of missed polyps was developed based on the validated GAN-synthesized images
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