scholarly journals Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5995
Author(s):  
Chen-Ming Hsu ◽  
Chien-Chang Hsu ◽  
Zhe-Ming Hsu ◽  
Feng-Yu Shih ◽  
Meng-Lin Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Narrow Band ◽  
Colorectal Polyp ◽  
Computer Assisted ◽  
Polyp Detection ◽  
Image Detection ◽  
Adenoma Detection ◽  
Incidence And Mortality ◽  
Rgb Images ◽  
Grayscale Images

Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis.

scholarly journals PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

2020 ◽  
Vol 10 (23) ◽  
pp. 8501
Author(s):  
Luisa F. Sánchez-Peralta ◽  
J. Blas Pagador ◽  
Artzai Picón ◽  
Ángel José Calderón ◽  
Francisco Polo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
White Light ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Polyp Detection ◽  
Test Set ◽  
Learning Techniques ◽  
Adenoma Detection ◽  
Comparison Results

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.

scholarly journals TOWARDS AN UNIFIED FRAMEWORK FOR VALIDATING DEEP LEARNING METHODS FOR COLORECTAL POLYPS: FIRST STEPS

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
L F Sánchez Peralta ◽  
J F Ortega Morán ◽  
Cr L Saratxaga ◽  
J B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Deep Learning ◽  
Colorectal Polyps ◽  
Polyp Detection ◽  
Unified Framework ◽  
Endoscopic Image ◽  
Learning Techniques ◽  
Great Utility ◽  
Adenoma Detection ◽  
Definition Of ◽  
Basic Features

Abstract INTRODUCTION Deep learning techniques have significantly contributed to the field of medical imaging analysis. In case of colorectal cancer, they have shown a great utility for increasing the adenoma detection rate at colonoscopy, but a common validation methodology is still missing. In this study, we present preliminary efforts towards the definition of a validation framework. MATERIAL AND METHODS Different models based on different backbones and encoder-decoder architectures have been trained with a publicly available dataset that contains white light and NBI colonoscopy videos, with 76 different lesions from colonoscopy procedures in 48 human patients. A computer aided detection (CADe) demonstrator has been implemented to show the performance of the models. RESULTS This CADe demonstrator shows the areas detected as polyp by overlapping the predicted mask on the endoscopic image. It allows selecting the video to be used, among those from the test set. Although it only present basic features such as play, pause and moving to the next video, it easily loads the model and allows for visualization of results. The demonstrator is accompanied by a set of metrics to be used depending on the aimed task: polyp detection, localization and segmentation. CONCLUSIONS The use of this CADe demonstrator, together with a publicly available dataset and predefined metrics will allow for an easier and more fair comparison of methods. Further work is still required to validate the proposed framework.

scholarly journals The National Endoscopy Database (NED) Automated Performance Reports to Improve Quality Outcomes Trial (APRIQOT) randomized controlled trial design

2020 ◽  
Vol 08 (11) ◽  
pp. E1545-E1552
Author(s):  
Jamie Catlow ◽  
Linda Sharp ◽  
Adetayo Kasim ◽  
Liya Lu ◽  
Matthew Brookes ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Real Time ◽  
Impact Analysis ◽  
Action Plan ◽  
Controlled Trial ◽  
Automated System ◽  
Polyp Detection ◽  
Randomized Controlled ◽  
Adenoma Detection ◽  
Incidence And Mortality

Abstract Background and study aims Colonoscopists with low polyp detection have higher post colonoscopy colorectal cancer incidence and mortality rates. The United Kingdom’s National Endoscopy Database (NED) automatically captures patient level data in real time and provides endoscopy key performance indicators (KPI) at a national, endoscopy center, and individual level. Using an electronic behavior change intervention, the primary objective of this study is to assess if automated feedback of endoscopist and endoscopy center-level optimal procedure-adjusted detection KPI (opadKPI) improves polyp detection performance. Methods This multicenter, prospective, cluster-randomized controlled trial is randomizing NHS endoscopy centres to either intervention or control. The intervention is targeted at independent colonoscopists and each center’s endoscopy lead. The intervention reports are evidence-based from endoscopist qualitative interviews and informed by psychological theories of behavior. NED automatically creates monthly reports providing an opadKPI, using mean number of polyps, and an action plan. The primary outcome is opadKPI comparing endoscopists in intervention and control centers at 9 months. Secondary outcomes include other KPI and proximal detection measures at 9 and 12 months. A nested histological validation study will correlate opadKPI to adenoma detection rate at the center level. A cost-effectiveness and budget impact analysis will be undertaken. Conclusion If the intervention is efficacious and cost-effective, we will showcase the potential of this learning health system, which can be implemented at local and national levels to improve colonoscopy quality, and demonstrate that an automated system that collects, analyses, and disseminates real-time clinical data can deliver evidence- and theory-informed feedback.

scholarly journals Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4593
Author(s):  
Cho-Lun Tsai ◽  
Arvind Mukundan ◽  
Chen-Shuan Chung ◽  
Yi-Hsun Chen ◽  
Yao-Kuang Wang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Detection Method ◽  
Narrow Band ◽  
Identification System ◽  
Single Shot ◽  
Test Results ◽  
Endoscopic Images ◽  
Rgb Images

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

scholarly journals SRGAN Assisted Encoder-Decoder Deep Neural Network for Colorectal Polyp Semantic Segmentation

2021 ◽  
Vol 35 (5) ◽  
pp. 395-401
Author(s):  
Mohan Mahanty ◽  
Debnath Bhattacharyya ◽  
Divya Midhunchakkaravarthy
Keyword(s):  
Deep Learning ◽  
Standard Procedure ◽  
Super Resolution ◽  
Semantic Segmentation ◽  
Colorectal Polyp ◽  
Polyp Detection ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Colon Cancers ◽  
Benchmark Datasets

Colon cancer is thought about as the third most regularly identified cancer after Brest and lung cancer. Most colon cancers are adenocarcinomas developing from adenomatous polyps, grow on the intima of the colon. The standard procedure for polyp detection is colonoscopy, where the success of the standard colonoscopy depends on the colonoscopist experience and other environmental factors. Nonetheless, throughout colonoscopy procedures, a considerable number (8-37%) of polyps are missed due to human mistakes, and these missed polyps are the prospective reason for colorectal cancer cells. In the last few years, many research groups developed deep learning-based computer-aided (CAD) systems that recommended many techniques for automated polyp detection, localization, and segmentation. Still, accurate polyp detection, segmentation is required to minimize polyp miss out rates. This paper suggested a Super-Resolution Generative Adversarial Network (SRGAN) assisted Encoder-Decoder network for fully automated colon polyp segmentation from colonoscopic images. The proposed deep learning model incorporates the SRGAN in the up-sampling process to achieve more accurate polyp segmentation. We examined our model on the publicly available benchmark datasets CVC-ColonDB and Warwick- QU. The model accomplished a dice score of 0.948 on the CVC-ColonDB dataset, surpassed the recently advanced state-of-the-art (SOTA) techniques. When it is evaluated on the Warwick-QU dataset, it attains a Dice Score of 0.936 on part A and 0.895 on Part B. Our model showed more accurate results for sessile and smaller-sized polyps.

Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study

2020 ◽  
Vol 91 (1) ◽  
pp. 104-112.e5 ◽  
Author(s):  
Wai K. Leung ◽  
Chuan-guo Guo ◽  
Michael K.L. Ko ◽  
Elvis W.P. To ◽  
Lung Yi Mak ◽  
...  
Keyword(s):  
Narrow Band ◽  
Narrow Band Imaging ◽  
Colorectal Polyp ◽  
Polyp Detection ◽  
Color Imaging ◽  
Linked Color Imaging

An artificial intelligence-based quality improvement system significantly improved the efficacy of computer-aided detection system in colonoscopy: A four group parallel study

Endoscopy ◽  
2021 ◽  
Author(s):  
Liwen Yao ◽  
Lihui Zhang ◽  
Jun Liu ◽  
Wei Zhou ◽  
Chunping He ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Detection System ◽  
Synergetic Effect ◽  
Colorectal Polyp ◽  
Controlled Study ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Adenoma Detection ◽  
Significant Difference ◽  
Computer Aided

Background and study aims: Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It is still debatable whether the combination of a computer-aided detection (CADe) system for colorectal polyp detection with a computer-aided quality improvement (CAQ) system for real-time withdrawal speed monitoring may result in additional benefits in the task of adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from the information overload. This study aims to evaluate the interaction effect on improving the adenoma detection rate (ADR). Patients and methods: This is a single-center, randomized, four-group parallel controlled study, performed in Renmin Hospital of Wuhan University. Between July 1, 2020 and Oct 15, 2020, 1076 participants were randomly allocated into four treatment groups [control: 271, CADe: 268, CAQ: 269 and CADe plus CAQ (COMBO): 268]. The primary outcome was the ADR. Results: The average ADR in the control, CADe, CAQ and COMBO groups was 14.76% (95% C.I. 10.54-18.98), 21.27% (95% C.I. 16.37-26.17), 24.54% (95% C.I. 19.39-29.68) and 30.6% (95% C.I. 25.08-36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group but not compared with the CAQ group (21.27% VS 30.6%, P=0.024, OR 1.284, 95%C.I. 1.033-1.596; 24.54%vs. 30.6%, P = 0.213, OR = 1.309, 95% C.I. 0.857-2, respectively). Conclusions: CAQ significantly improved the efficacy of CADe in a four-group parallel controlled study. No significant difference in the ADR or PDR was found between the CAQ and COMBO groups.

scholarly journals Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

2020 ◽  
Vol 36 (6) ◽  
pp. 428-438
Author(s):  
Thomas Wittenberg ◽  
Martin Raithel
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lesion Detection ◽  
Screening Colonoscopy ◽  
Computer Assisted ◽  
Polyp Detection ◽  
The Past

<b><i>Background:</i></b> In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. <b><i>Summary:</i></b> With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. <b><i>Key Messages:</i></b> Research<b><i></i></b>of<b><i></i></b>image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

951e – A Real-Time Automatic Deep Learning Polyp Detection System Increases Polyp and Adenoma Detection During Colonoscopy: A Prospective Double-Blind Randomized Study

2019 ◽  
Vol 156 (6) ◽  
pp. S-1511 ◽  
Author(s):  
Guanyu Zhou ◽  
Xiaogang Liu ◽  
Tyler M. Berzin ◽  
Jeremy R. Glissen Brown ◽  
Liangping Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection System ◽  
Randomized Study ◽  
Polyp Detection ◽  
Double Blind ◽  
Adenoma Detection
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