A V-Net Based Deep Learning Model for Segmentation and Classification of Histological Images of Gastric Ablation.

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Abstract

Gastric motility disorders are associated with bioelectrical abnormalities in the stomach. Recently, gastric ablation has emerged as a potential therapy to correct gastric dysrhythmias. However, the tissue-level effects of gastric ablation have not yet been evaluated. In this study, radiofrequency ablation was performed in vivo in pigs (n=7) at temperature-control mode (55-80°C, 5-10 s per point). The tissue was excised from the ablation site and routine H&E staining protocol was performed. In order to assess tissue damage, we developed an automated technique using a fully convolutional neural network to segment healthy tissue and ablated lesion sites within the muscle and mucosa layers of the stomach. The tissue segmentation achieved an overall Dice score accuracy of 96.18 ± 1.0 %, and Jacquard score of 92.77 ± 1.9 %, after 5-fold cross validation. The ablation lesion was detected with an overall Dice score of 94.16 ± 0.2 %. This method can be used in combination with high-resolution electrical mapping to define the optimal ablation dose for gastric ablation.Clinical Relevance-This work presents an automated method to quantify the ablation lesion in the stomach, which can be applied to determine optimal energy doses for gastric ablation, to enable clinical translation of this promising emerging therapy.