Fully convolutional neural networks for polyp segmentation in colonoscopy

Brandão, Patrick; Mazomenos, Evangelos B.; Ciuti, Gastone; Caliò, Renato; Bianchi, Federico; Menciassi, Arianna; Dario, P.; Koulaouzidis, Anastasios; Arezzo, Alberto; Stoyanov, Danail

doi:10.1117/12.2254361

Cited by 96 publications

(59 citation statements)

References 8 publications

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“…A low dice score may occur when there is a heavy class imbalance, such as in this dataset, where ~1% of pixels are in the polyp class compared to ~99% in the background class. A similar approach was reported by Brandao et al 66 They obtained a relatively high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively. Consequently, polyp segmentation in endoscopy using DL remains a challenging issue.…”

Section: Ai-based Segmentation In Endoscopysupporting

confidence: 68%

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Choi

Shin

Jung³

et al. 2020

Clin Endosc

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Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology. clin endosc 2020;53:117-126

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Section: Ai-based Segmentation In Endoscopysupporting

confidence: 68%

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Choi

Shin

Jung³

et al. 2020

Clin Endosc

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“…First, in terms of the most challenging dataset, which is provided by ETIS laboratory and Lariboisiere Hospital-APHP. In order to challenge the previous accomplishment on ETIS-Larib dataset, Brandao et al [10]'s methodology follows the same data guidelines and restrictions with which is given in the e 2015 MICCAI sub-challenge. In the training phase, the author used 4664 images and its corresponding labels which contains at least one polyp and later they use ETIS-Larib [8] to evaluate the performance of model.…”

Section: ) Results On the Etis-larib Datasetmentioning

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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“…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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Fully convolutional neural networks for polyp segmentation in colonoscopy

Cited by 96 publications

References 8 publications

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

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

Colorectal Polyp Segmentation by U-Net with Dilation Convolution

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