Colorectal polyp segmentation using front propagation on surfaces guided by shape

Krishnan, Karthik; Soniwal, Yogesh; Madrosiya, Akshay; Desai, Nasir

doi:10.1109/embc.2015.7319046

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In recent years, more and more researchers have been involved in the study of polyp segmentation and put forward various methods. Some researchers used different machine learning methods to get good segmentation results . And some researchers improved several architectures of Full Convolution Networks (FCNs) and Convolutional Neural Network (CNN) .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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Section: Introductionmentioning

confidence: 99%

“…Some researchers used different machine learning methods to get good segmentation results. [6][7][8][9] And some researchers improved several architectures of Full Convolution Networks (FCNs) and Convolutional Neural Network (CNN). [10][11][12] Nguyen et al 13 proposed a method based on multiple encoder-decoder network.…”

Section: Introductionmentioning

confidence: 99%