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Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

机译：结肠镜检查的自动息肉分割图像：一种基于卷积神经网络和集合学习的方法

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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.
机译：目的是自动和有效地分段结肠镜检查息肉图像的病变区域，已经呈现了息肉分段方法。方法采用UNET-VGG，SEGNET-VGG和PSPNET提出了预先训练卷积神经网络的集合模型。首先，由第一10层VGG16作为左半部分的收缩路径获得UNET-VGG。然后，通过微调传输学习vgg16获取SEGNET-VGG，使用VGG16的第一13层作为SEGNET的编码器并组合SEGNET的原始解码器。通过调整UNET-VGG，SEGNET-VGG和PSPNET的输入大小，可以正确地馈送到三个网络模型的预处理数据。这三种模型用作培训和分段数据集的基本培训师。基于集合学习算法，重量投票方法用于集成与单​​个基本训练器对应的分段结果。结果IOU和骰子相似度分数用于评估CVC300的分割质量，具有300图像，CVC-ClinicDB，具有612个图像，以及196个图像的Etis-LaribpolypdB。从实验结果，通过所提出的CVC300数据集获得的IOU和骰子可分别达到96.16％和98.04％，CVC-ClinicDB数据集的IOU和骰子可达96.66％和98.30％，分别为ETIS-LaribpolypdB Datasets的IOU和骰子，分别可以达到96.95％和98.45％。我们的方法中iou和骰子的评估表现出比以前的方法更高的准确性。结论实验结果表明，与单一基本培训师相比，该方法相应地改善了IOU和骰子。改善范围为1.98％-6.38％。所提出的集合学习成功地在自动息肉分段中，这可能有助于建立更多的息肉数据集。

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