United European gastroenterology journal · 2019
The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy.
СтатьяПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
de Groof Jeroen, van der Sommen Fons, van der Putten Joost, Struyvenberg Maarten R, Zinger Sveta, Curvers Wouter L, Pech Oliver, Meining Alexander, Neuhaus Horst, Bisschops Raf, Schoon Erik J, de With Peter H, Bergman Jacques J
Аннотация
BACKGROUND: Computer-aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia.
AIM: To develop a CAD system using endoscopic images of Barrett's neoplasia.
METHODS: White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non-dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images.The overlap area of at least four delineations was labelled as the 'sweet spot'. The area with at least one delineation was labelled as the 'soft spot'. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave-one-out cross-validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red-flag indication score).
RESULTS: Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red-flagged the soft spot in 100 and 90%, respectively.
CONCLUSION: This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real-time automated detection of Barrett's neoplasia.
AIM: To develop a CAD system using endoscopic images of Barrett's neoplasia.
METHODS: White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non-dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images.The overlap area of at least four delineations was labelled as the 'sweet spot'. The area with at least one delineation was labelled as the 'soft spot'. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave-one-out cross-validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red-flag indication score).
RESULTS: Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red-flagged the soft spot in 100 and 90%, respectively.
CONCLUSION: This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real-time automated detection of Barrett's neoplasia.