Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach
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Background/Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas havefailed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI modelwith inference speeds faster than 25 frames per second that maintains a high level of accuracy. Methods: Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Fourstrategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, animage preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third,data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIMareas in real time. The results were analyzed using different validity values. Results: From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity,specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%,80%, 82%, 92%, 87%, and 57%, respectively. Conclusions: The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization,and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.
Abstract
Background/Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas havefailed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI modelwith inference speeds faster than 25 frames per second that maintains a high level of accuracy. Methods: Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Fourstrategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, animage preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third,data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIMareas in real time. The results were analyzed using different validity values. Results: From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity,specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%,80%, 82%, 92%, 87%, and 57%, respectively. Conclusions: The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization,and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.
참고문헌 (36)
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