Colorectal Polyp Segmentation by U-Net with Dilation Convolution

Sun, Xiuhong; Zhang, Pengfei; Wang, Dechun; Cao, Yu; Liu, Benyuan

doi:10.1109/icmla.2019.00148

Cited by 53 publications

(11 citation statements)

References 29 publications

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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.…”

Section: Resultsmentioning

confidence: 98%

“…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. Although the same test data were used in [ 69 ] as in this paper, compared to the results reported here, an additional 10,025 images from the CVC-Clinic VideoDB were used to train the U-Net with the dilatation convolution method. The results reported in [ 70 ] are better than for any other method listed in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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

2020

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“…Models based on dilated convolution architecture: Sun et al 58 used dilated convolution in the last block of the encoder while Safarov et al 59 used in all encoder blocks. Though 59 used a mesh of attention blocks and residual block as a decoder, both methods tested there model on CVC-ClinicDB achieving F1-score of 96.106 and 96.043, respectively.…”

Section: /26mentioning

confidence: 99%

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Ali¹,

Ghatwary²,

Jha³

et al. 2022

Preprint

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Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets.Author contributions S. Ali conceptualised the work, led the challenge and workshop, prepared the dataset, software and performed all analyses. S. Ali, N. Ghatwary and D. Jha contributed in data annotations. T. de Lange, J.E. East, S. Realdon, R. Cannizzaro, D. Lamarque were involved providing colonoscopy data and in the validation and quality checks of the annotations used in this challenge. Challenge participants (E

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“…More recently, encoder-decoder based models, such as U-Net [4], UNet++ [22], and ResUNet++ [23], have gradually come to dominate the field with excellent performance. Sun et al [24] introduced a dilated convolution to extract and aggregate highlevel semantic features with resolution retention for improving the encoder network. Psi-Net [25] introduced a multi-task segmentation model that combines contour prediction and distance map estimation to assist segmentation mask prediction.…”

Section: A Polyp Segmentationmentioning

confidence: 99%

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Dong¹,

Wang²,

Fan³

et al. 2021

Preprint

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Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing effective mechanism for fusing these features. Different from existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three novel modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), a and similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features, while the CIM is applied to capture polyp information disguised in low-level features. With the help of the SAM, we extend the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects) than existing methods, and achieves the new state-of-the-art performance. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.

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Colorectal Polyp Segmentation by U-Net with Dilation Convolution

Cited by 53 publications

References 29 publications

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

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

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

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