Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pål

doi:10.1109/access.2021.3063716

Cited by 198 publications

(82 citation statements)

References 62 publications

(85 reference statements)

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“…The first reason is that the research questions were different. We focused on the classification according to the histopathological ground truths, however, several groups dedicated their efforts to detect polyps on the images (39,40). Many studies have been conducted on computer-aided detection to decrease the missing rate of colon polyps.…”

Section: Discussion Discussionmentioning

confidence: 99%

Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

Dogan

Yılmaz

2021

The EuroBiotech Journal

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Determination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.

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Section: Discussion Discussionmentioning

confidence: 99%

Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

Dogan

Yılmaz

2021

The EuroBiotech Journal

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“…It would be interesting for future work to investigate more recent models like ColonSegNet [26], NanoNet [27] RESUNET++ [28], and DoubleUNet [24] as well as larger image sizes, as our experiments were limited to image sizes of 128x128 and the standard U-NET and Pix2Pix models. There are also other unsupervised models related to Pix2Pix which may be of interest.…”

Section: Discussionmentioning

confidence: 99%

Pyramidal Segmentation of Medical Images using Adversarial Training

Næss

Thambawita

Hicks

et al. 2021

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval

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Colorectal cancer is a severe health issue globally and a significant cause of cancer-related mortality, but it is treatable if found at an early stage. Early detection is usually done through a colonoscopy, where clinicians search for cancer precursors called polyps. Research has shown that clinicians miss between 14% and 30% of polyps during standard screenings of the gastrointestinal tract. Furthermore, once the polyps have been found, clinicians often overestimate the size of the polyps. In this respect, automatic analysis of medical images for detecting and locating polyps is a research area where machine learning has excelled in recent years. Still, current models have much room for improvement. In this paper, we propose a novel approach based on learning to segment within several grids, which we introduce to U-Net and Pix2Pix architectures. In short, we have experimented using several grid sizes, and using two opensource polyp segmentation datasets for cross-data training and testing. Our results suggest that segmentation at lower resolutions produces better results at the cost of less precision, which proved useful for the cases where higher precision segmentations gave limited results. Generally, compared to traditional U-Net and Pix2Pix, our grid-based approaches improve segmentation performance.

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“…Priotising efficiency over performance, PolypSegNet introduces the depth dilated inception module, enabling efficient feature extraction across a range of receptive field sizes [ 28 ]. Similarly, ColonSegNet is a light-weight network that includes residual connections and channel attention to achieve real-time polyp segmentation [ 29 ]. PraNet uses a two-step process that involves initial localisation of the polyp area, followed by progressive refining of the polyp boundary, resembling the method by which humans identify polyps [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

Yeung

Sala

Schönlieb

et al. 2021

Computers in Biology and Medicine

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Background Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. Method In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net incorporates several further architectural modifications, including the addition of short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, designed to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. We first perform a series of ablation studies and then evaluate the Focus U-Net on the CVC-ClinicDB and Kvasir-SEG datasets separately, and on a combined dataset of all five public datasets. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics. Results Our model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14 % and 15 % improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively. Conclusions This study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive colorectal cancer screening and more broadly to other biomedical image segmentation tasks similarly involving class imbalance and requiring efficiency.

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Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

Cited by 198 publications

References 62 publications

Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

Pyramidal Segmentation of Medical Images using Adversarial Training

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

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