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Purpose To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented. Methods An ensemble model of pretrained convolutional neural networks was proposed, using Unet‐VGG, SegNet‐VGG, and PSPNet. Firstly, the Unet‐VGG is obtained by the first 10 layers of VGG16 as the contraction path of the left half of the Unet. Then, the SegNet‐VGG is acquired by fine‐tuned transfer learning VGG16, using the first 13 layers of VGG16 as the encoder of the SegNet and combined the original decoder of the SegNet. By adjusting the input size of the Unet‐VGG, SegNet‐VGG, and PSPNet, the preprocessed data can be correctly fed to the three network models. The three models are used as the basic trainer to train and segment the datasets. Based on the ensemble learning algorithm, the weight voting method is used to ensemble the segmentation results corresponding to single basic trainer. Results Both IoU and DICE similarity score were used to evaluate the segmentation quality for cvc300 with 300 images, CVC‐ClinicDB with 612 images, and ETIS‐LaribPolypDB with 196 images. From the experimental results, the IoU and DICE obtained by the proposed method for the cvc300 datasets can reach up to 96.16% and 98.04%, respectively, the IoU and DICE for the CVC‐ClinicDB datasets can reach up to 96.66% and 98.30%, respectively, whereas the IoU and DICE for the ETIS‐LaribPolypDB datasets can reach up to 96.95% and 98.45%, respectively. Evaluation of the IoU and DICE in our methods shows higher accuracy than previous methods. Conclusions The experimental results show that the proposed method improved correspondingly in IoU and DICE compared to a single basic trainer. The range of improvement is 1.98%–6.38%. The proposed ensemble learning succeeds in automatic polyp segmentation, which potentially helps to establish more polyp datasets.

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Although endoscopic ultrasound (EUS)-guided transmural drainage of pancreatic fluid collections with metal stents is generally preferred over plastic stents, its superiority among different types of metal stents has not yet been well studied. We conducted this study to compare clinical outcomes and complications of a novel self-expanding biflanged metal stent (BFMS) and a traditional-shaped tubular metal stent (TMS) in treating pancreatic pseudocyst (PPC).This was a retrospective analysis on consecutive patients with PPC underwent EUS-guided transmural drainage with either TMS or BFMS in a single tertiary center with expertise in management of complex biliary and pancreatic problems. The technical and functional success rate, reintervention, complications, and recurrence rate were evaluated.From September 2013 to January 2018, 125 patients (66.4% male, median age 47 years) underwent EUS-guided transmural drainage for PPC. Among them, 49 used TMS and 76 used BFMS. All patients met the inclusion criteria that cyst diameter was >6 cm or the distance between cyst and stomach wall was shorter than 1 cm. There was no difference in technical success (98% vs 97.4%, P = 1.0) or functional success rate (87.8% vs 92.1%, P = .54) using 2 types of metal stents. However, more procedure related complications occurred in TMS than in BFMS group. TMS group had a much higher migration rate than BFMS group (14.6% vs 0, P = .001), even though there was no significant difference in bleeding, infection, or death rate between 2 groups. With similar clinical outcomes, TMS group required more additional plastic stent placement than BFMS group for better drainage.TMS and BFMS placement can both be considered as methods of endoscopic transmural PPC drainage with equal efficacy, whereas BFMS could be preferred for fewer complications or less need of additional plastic stent placement.