Colorectal polyp segmentation using a fully convolutional neural network

Li, Qiaoliang; Yang, Guangyao; Chen, Zhewei; Huang, Bin; Chen, Liangliang; Xu, Danfeng; Zhou, Xuechang; Su, Zhong; Zhang, Huisheng; Wang, Tianfu

doi:10.1109/cisp-bmei.2017.8301980

Cited by 66 publications

(36 citation statements)

References 3 publications

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“…Furthermore, we also report the results from a second publicly available dataset (CVC-ColonDB). In addition, to show the effectiveness of our method when applied to additional datasets, we also used many datasets to meet different goals ; for example, we used a combination of CVC-ColonDB and CVC-ClinicDB, which contains 912 images with associated polyps, to compare our method with that of Li et al [33].…”

Section: A Datasetsmentioning

confidence: 99%

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

Nguyen

Lee

2020

IEEE Access

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Colorectal cancer has become one of the most common cause of cancer mortality worldwide, with a five-year survival rate of over 50%. Additionally, the potential of some common polyp types to progress to colorectal cancer is considered high. Colonoscopy is the most common method for finding and removing polyps. However, during colonoscopy, a significant number of polyps is missed as a result of human error mistakes. Thus, this study was primarily motivated by the need to obtain an early and accurate diagnosis of polyps detected in colonoscopy images. In this paper, we propose a new polyp segmentation method based on an architecture of multi-model deep encoder-decoder networks called MED-Net. Not only does this architecture obtain multi-level contextual information by extracting discriminative features at different effective fields-of-view and multiple image scales, it also can substantially do upsample more correctly to produce better prediction. It is also able to capture more accurate polyp boundaries by using multiscale effective decoders. Moreover, we also present a complementary strategy for improving the method's segmentation performance based on a combination of a boundary-aware data augmentation method and an effective weighted loss function. The purpose of this strategy is to allow our deep learning network to sequentially focus on poorly defined polyp boundaries, which are caused by the non-specular transition zone between the polyp and non-polyp regions. To provide a general view of the proposed method, our network was trained and evaluated on four well-known dataset CVC-ColonDB, CVC-ClinicDB, ASU-Mayo Clinic Colonoscopy Video Database, and ETIS-LaribPolypDB. Our results show that our MED-Net significantly outperforms state-of-the-art methods.

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Section: A Datasetsmentioning

confidence: 99%

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

Nguyen

Lee

2020

IEEE Access

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“…Recently, FCN has been introduced and become a popular technique in medical image segmentation. Because of its promising potential in medical image segmentation, a number of studies used FCN with different backbone networks for colorectal polyp segmentation and obtained promising results [14], [17]. U-Net [12], proposed by Ronneberger et al is another important method in semantic image segmentation and it was initially proposed for biomedical image segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Some other studies used semantic image segmentation methods such as FCN (Fully Convolutional Networks) [11], U-Net [12] or SegNet [13] for colorectal polyp detection and have shown great potential in this application [14]- [17]. However, these methods were constructed with traditional CNN structure which contains repeated max-pooling or downsampling (striding) operations.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Segmentation by U-Net with Dilation Convolution

Sun

Zhang

Wang

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. The model we design consists of an encoder to extract multi-scale semantic features and a decoder to expand the feature maps to a polyp segmentation map. We improve the feature representation ability of the encoder by introducing the dilated convolution to learn high-level semantic features without resolution reduction. We further design a simplified decoder which combines multiscale semantic features with fewer parameters than the traditional architecture. Furthermore, we apply three post processing techniques on the output segmentation map to improve colorectal polyp detection performance. Our method achieves state-of-theart results on CVC-ClinicDB and ETIS-Larib Polyp DB. 1 With VGG-16 backbone. 2 Dilated ResFCN + SE-Unet. 3 With Inception ResNet [34] backbone.

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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%

“…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%

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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Colorectal polyp segmentation using a fully convolutional neural network

Cited by 66 publications

References 3 publications

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

Colorectal Polyp Segmentation by U-Net with Dilation Convolution

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

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