This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.
Capsule endoscopy (CE) is indicated as a first-line clinical examination for the detection of small-bowel pathology, and there is an ever-growing drive for it to become a method for the screening of the entire gastrointestinal tract (GI). Although CE's main function is diagnosis, the research for therapeutic capabilities has intensified to make therapeutic capsule endoscopy (TCE) a target within reach. This manuscript presents the research evolution of CE and TCE through the last 5 years and describes notable problems, as well as clinical and technological challenges to overcome. This review also reports the state-of-the-art of capsule devices with a focus on CE research prototypes promising an enhanced diagnostic yield (DY) and treatment. Lastly, this article provides an overview of the research progress made in software for enhancing DY by increasing the accuracy of abnormality detection and lesion localisation. The future of capsule endoscopy-experimental devices for diagnosis and therapy Gastroenterology Review
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