Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images

Nguyen, Quang Hung; Lee, Sang‐Woong

doi:10.1109/aike.2018.00048

Cited by 23 publications

(12 citation statements)

References 5 publications

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“…In Kang et al, the IoU are 66.07% for ETIS‐LaribPolypDB and 69.46% for CVC‐ColonDB. The DICE is 81% for CVC‐ColonDB dataset in Akbari et al, and the DICE is 88.9% for ETIS‐LaribPolypDB dataset in Nguyen et al The IoU and the DICE in our methods show higher accuracy for image segmentation. It is helpful to establish the database for polyp segmentation and polyp recognition.…”

Section: Discussionmentioning

confidence: 65%

“…[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. Xiao et al 14 parallel combined Long Short-Term Memory networks and Dee-pLab_v3 to augment the signal of the polyps' location.…”

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: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…From the results in Table 4 , it can be seen that the multiple encoder-decoder network (MEDN) [ 68 ] and the U-Net with dilatation convolution methods [ 69 ] have a comparable performance to the method described in this paper. However, it should be noted that for the reported results the Dilated ResFCN method used only 355 images for training, whereas in [ 68 ] 612 images were used. Furthermore, the mean Dice coefficient results reported here for the Dilated ResFCN used 612 test images, whereas results reported in [ 68 ] are based on only 196 test images.…”

Section: Resultsmentioning

confidence: 98%

Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

2020

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Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications—lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method’s design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge.

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“…The deconvolutional layer was applied to smoothen the segmentation maps and obtain final high-resolution output. Nguyen and Lee (2018) proposed a poly segmentation method based on architecture of a multiple deep Encoder-decoder networks called CDED-net. The system captures object boundaries using multi-scale decoders which is integrated with both boundaries using multi-scale decoders which is integrated with both boundary-emphasization data augmentation method and a novel loss function.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Salih

Viriri

2020

Image Anal Stereol

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Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

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Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images

Cited by 23 publications

References 5 publications

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

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

Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

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