Polyp Images Research Articles

S212 Comparing Polyp Detection Rate Between Water Exchange and Air Insufflation by a Computer-aided Detection Algorithm: An Analysis of Withdrawal Phase Videos from a Randomized Controlled Trial

Introduction: Water exchange (WE) improves polyp (PDR) and adenoma detection rate (ADR) compared with air insufflation [GIE 2021;93:1411], but still misses polyps due to human limitations [JCG 2021, in press]. A computer-aided detection (CADe) algorithm can overcome human omissions, but is limited by false positives (FPs) [Lancet Gastroenterol & Hepatol 2019;4:71], which might be reduced by salvage-cleaning effect provided by insertion WE [Tzu-Chi Med J 2021;33:108]. We hypothesized that the strengths of CADe and WE can complement the weaknesses of each other. Their combination can enhance PDR and ADR and reduce false positive rate (FPR). Methods: We used a locally developed convolutional neural network with YOLOv4 to build a CADe model. Images of polyps (n=15961) were divided in 80% and 20% for training and testing. Prior to real-time application, we validated the model in detecting polyps, using edited and coded withdrawal phase videos of the right colon from a RCT comparing right colon adenoma detection between WE and air insufflation. The polyps detected by the CADe were confirmed by the consensus of 2 blinded reviewers (experienced endoscopists). We also developed a computer-aided diagnosis (CADx) model based on YOLOv4 to characterize the detected polyps as adenomas or not. The primary and secondary outcomes were PDR and the FPR, respectively. Results: A total of 245 coded videos of colonoscopies inserted with WE (n=123) or air insufflation (n=122) were analyzed. CADe achieved significantly higher PDR in the WE group (61.8%) than in the air insufflation group (40.2%), P=0.0009. CADe also had significantly higher additional PDR in the WE group (30.1% vs 12.3%, P=0.001). The CADx assessed ADR was significantly higher in the WE group than that in the air insufflation group (46.3% vs 30.3%, P=0.018), after the detected polyps were characterized by the CADx. (Table a) The mean numbers of FPs related to feces [1.78 (1.67) vs 2.09 (2.09), P=0.007] and bubbles [0.53 (0.89) vs 1.25 (2.45), P=0.001] were significantly lower in the WE group than in the air insufflation group. (Table b). Conclusion: The data confirmed the hypothesis that the strengths of CADe and WE complemented the weaknesses of each other, enhanced PDR and ADR and reduced FPR. The locally developed CADe and CADx are validated for polyp detection and adenoma assignment for real-time application. Furthermore, WE provides an optimal platform for the computer-assisted algorithms.Table 1.: ADR, adenoma detection rate; All the percentages calculated according to respective total number of patients for each group; PDR, polyp detection rate; Total numbers of polyps were based on those found by the performing endoscopist plus those detected by the CADe; Proportion of additional polyps found by CADe equals numbers of additional polyp (adenomas) found by CADe divided by total numbers of polyps (adenomas). Others: ileocecal valve, cecum, polypectomy site, suction marks and drug pills. † Mann-Whitney test; ††Fisher exact test.

Evaluation of colon mucosa using screening colonoscopy and flexible spectral imaging color enhancement in patients with long lasting ulcerative colitis

AimTo determine which flexible spectral imaging color enhancement (FICE) channel best visualizes colon mucosa in ulcerative colitis (UC) and to compare FICE imaging with standard imaging.MethodsThe study enrolled patients with ulcerative colitis in remission who had inflammatory bowel disease for at least 8 years. All patients underwent screening colonoscopy. The entire colon, especially the suspicious areas in terms of dysplasia, were imaged with standard endoscopy and FICE. Random and target biopsies were obtained. Histopathological diagnosis was made and image patterns were evaluated. Seven endoscopists evaluated normal, colitis, and polyp images obtained with FICE.ResultsOne hundred and twenty-three colon segments were evaluated and 1831 images were obtained from 18 patients. A total of 1652 images were FICE and 179 were standard images. Separate FICE images were obtained for normal colon mucosa, polypoid lesions, and colitis areas. Normal colon mucosa was best visualized using the second, sixth, and ninth FICE channel; polyps using the third, seventh, and ninth channel; and colitis using the second, third, and ninth channel. When all images were analyzed, the second and ninth channel were significantly better than the other channels. A total of 584 biopsies were obtained, including 492 (84.2%) random biopsies and 92 (15.7%) target biopsies. Random biopsies detected no dysplasia, but target biopsies detected low-grade dysplasia in three diminutive polyps.ConclusionFICE was not significantly better at dysplasia screening than the standard procedure, but it effectively detected diminutive polyps and evaluated surface patterns without using magnification. FICE might contribute to the assessment of inflammation severity in patients with UC in clinical remission. However, more extensive studies are necessary to confirm these findings.

S133 Video Validation of a Novel Multi-Class Neural Network for In-situ Polyp Pathology Characterization

Introduction: Polyp pathology is essential in determining malignancy risk, surveillance intervals, and quality measurements in colonoscopy. The majority of polyps are resected and characterized manually by pathologists, which has a large cost and time burden. In-situ polyp characterization can potentially reduce costs and facilitate point-of-care decision-making. We previously developed and validated an optical pathology deep neural network for distinguishing adenomas from serrated polyps. Our goal was to create and validate a multiclass neural net to further distinguish serrated polyps into categories of hyperplastic and sessile serrated polyps. Methods: Our training set included 1,000 unique polyp video clips and 25,000 freeze-frame polyp images annotated for polyp pathology, including nearly 6000 unique adenomas, 3000 unique hyperplastic polyps, and 1500 unique sessile serrated polyps. A 6-step multi-stage training pipeline was devised to detect and extract polyp pathology features from individual frames and classify polyp pathology using Multiple Instance Learning. For validation, we utilized a fresh set of deidentified 171 video clips of unique polyps with known pathology. Results: The multi-class optical pathology system correctly characterized 94/99 (95%) polyps as adenoma and 51/59 (86.4%) as serrated. The negative predictive value (NPV) was 91.1%, positive predictive value (PPV) was 92.2%, and accuracy was 91.8%. Among serrated polyps, 16/16 (100%) were correctly characterized as SSP and 31/35 (88.5%) as HPs, with an NPV of 100%, PPV of 80%, and accuracy of 92.3%. Conclusion: Our optical pathology neural network accurately characterizes adenomas, HPs, and SSPs in-situ with an accuracy of >90%. Next-step prospective multicenter validation studies will determine if in-situ polyp characterization can automate ADR recording, provide accurate point-of-care surveillance intervals, and meet thresholds to reduce pathology cost through resect-and-discard or leave-alone strategies, with a potential cost savings of more than $1 billion per year.Figure 1.: Real-time sample images from the optical pathology system with confidence level. A) Hyperplastic polyp, B) SSA, C) AdenomaTable 1.: 3x3 confusion matrix comparing AI optical path results with true pathology results, including performance parameters.

Automatic image and text-based description for colorectal polyps using BASIC classification

Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment.

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning.

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.

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

BackgroundColonoscopy 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. MethodIn 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. ResultsOur 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. ConclusionsThis 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.

Utility of the narrow-band imaging international colorectal endoscopic classification for optical diagnosis of colorectal polyp histology in clinical practice: a retrospective study

BackgroundNarrow-band imaging (NBI) highlights the surface structures and vessels of colorectal polyps and is useful for determining the polyp histology. The narrow-band imaging international colorectal endoscopic (NICE) classification is a diagnostic tool for determining colorectal polyp histology based on NBI without optical magnification. In this study, we aimed to investigate the value of each type of the NICE classification for determining colorectal polyp histology using endoscopy data accumulated in a clinical setting.MethodsEndoscopy data for 534 colorectal polyps (316 patients) treated at our facility were retrospectively analyzed. First, we investigated the diagnostic performance of each type of the NICE classification for the optical diagnosis of colorectal polyp histology. The procedures were performed by experienced endoscopists using high-definition colonoscopy without optical magnification. Second, inter-observer and intra-observer agreements were assessed after providing experts and non-experts with a short lecture on the NICE classification. Using 50 fine NBI images of colorectal polyps without optical magnification, the inter-observer and intra-observer agreements between five experts and five non-experts were assessed.ResultsThe sensitivity, specificity, and accuracy values were 86.0%, 99.6%, and 98.5% for NICE type 1 lesions; 99.2%, 85.2%, and 97.8% for NICE type 2 lesions; and 81.8%, 99.6%, and 99.3% for NICE type 3 lesions, respectively. The inter-observer and intra-observer agreements ranged from substantial to excellent for both experts and non-experts.ConclusionsThe NICE classification had good diagnostic ability in terms of determining the polyp histology and demonstrated a high level of reproducibility among experts and non-experts. Thus, the NICE classification is a useful clinical tool that can be used without optical magnification.

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning.

BACKGROUNDArtificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice.AIMTo train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.METHODSWith high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.RESULTSThe CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time (P = 0.32; P = 0.16, respectively).CONCLUSIONCADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.

Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use.

Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The current study used a fast object detection algorithm, known as the YOLOv3 algorithm, to achieve real-time polyp detection on a laptop. In addition, we evaluated and classified the causes of false detections to further improve accuracy. The YOLOv3 algorithm was trained and validated with 6038 and 2571 polyp images, respectively. Videos from live colonoscopies in a tertiary center and those obtained from public databases were used for the training and validation sets. The algorithm was tested on 10 unseen videos from the CVC-Video ClinicDB dataset. Only bounding boxes with an intersection over union area of >0.3 were considered positive predictions. Polyp detection rate in our study was 100%, with the algorithm able to detect every polyp in each video. Sensitivity, specificity, and F1 score were 74.1%, 85.1%, and 83.3, respectively. The algorithm achieved a speed of 61.2frames per second (fps) on a desktop RTX2070 GPU and 27.2fps on a laptop GTX2060 GPU. Nearly a quarter of false negatives happened when the polyps were at the corner of an image. Image blurriness accounted for approximately 3% and 9% of false positive and false negative detections, respectively. The YOLOv3 algorithm can achieve real-time poly detection with high accuracy and speed on a desktop GPU, making it low cost and accessible to most endoscopy centers worldwide.

Gallbladder Polyp Classification in Ultrasound Images Using an Ensemble Convolutional Neural Network Model

Differential diagnosis of true gallbladder polyps remains a challenging task. This study aimed to differentiate true polyps in ultrasound images using deep learning, especially gallbladder polyps less than 20 mm in size, where clinical distinction is necessary. A total of 501 patients with gallbladder polyp pathology confirmed through cholecystectomy were enrolled from two tertiary hospitals. Abdominal ultrasound images of gallbladder polyps from these patients were analyzed using an ensemble model combining three convolutional neural network (CNN) models and a 5-fold cross-validation. True polyp diagnosis with the ensemble model that learned only using ultrasonography images achieved an area under receiver operating characteristic curve (AUC) of 0.8960 and accuracy of 83.63%. After adding patient age and polyp size information, the diagnostic performance of the ensemble model improved, with a high specificity of 88.35%, AUC of 0.9082, and accuracy of 87.61%, outperforming the individual CNN models constituting the ensemble model. In the subgroup analysis, the ensemble model showed the best performance with AUC of 0.9131 for polyps larger than 10 mm. Our proposed ensemble model that combines three CNN models classifies gallbladder polyps of less than 20 mm in ultrasonography images with high accuracy and can be useful for avoiding unnecessary cholecystectomy with high specificity.

A self-attention based faster R-CNN for polyp detection from colonoscopy images

At present, the incidence rate of colorectal cancer (CRC) is increasing year by year. It has always affected people's physical and mental health and quality of life. How to improve the detection ability of polyp plays a key role in colonoscopy. In order to solve these problems, in this paper, we first enhance the contrast of the input image by well distinguishing the foreground from the background in order to improve the saliency of the polyp regions. Then, we feed the enhanced image into an improved Faster R-CNN architecture comprised three processing modules for feature extraction, region proposal generation, and polyp detection, respectively. In order to further improve the quality, as well as the feature abstraction capability of the feature maps produced by the feature extraction network, we append an attention module to attend to the useful feature channels and weaken the contributions of the helpless feature channels. The experimental results demonstrate that the accuracy of the proposed polyp detection network is greatly improved compared with the existing algorithms, and the network not only can accurately identify polyps of varying sizes and conditions in single polyp images, but also can achieve excellent performance in handling multiple polyp images. This paper will be greatly helpful to alleviate the missed diagnosis of clinicians in the process of endoscopic examination and disease treatment, as well as providing effective assistance for the early diagnosis, treatment and prevention of the CRC, which is also of great significance to the clinical work of physicians.

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification.

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.

Value of Contrast-enhanced Ultrasound in Differential Diagnosis between Cholesterol Polyp and Gallbladder Adenoma

Objective To compare the performance of contrast-enhanced ultrasound(CEUS)and ultrasound(US)in the differential diagnosis between cholesterol polyps and gallbladder adenomas. Methods A total of 136 patients with gallbladder polyp lesions(GPLs)and undergoing cholecystectomy in the First Medical Center of Chinese PLA General Hospital from January 2019 to October 2020 were retrospectively analyzed.All the patients underwent US and CEUS examinations before cholecystectomy.US and CEUS images of cholesterol polyps and gallbladder adenomas were compared for the evaluation of the performance of CEUS in the diagnosis of gallbladder adenomas. Results The 136 cases of GPLs included 95 cases of cholesterol polyps and 41 cases of gallbladder adenomas.Cholesterol polyps and gallbladder adenomas showed significant differences in the maximum size of GPLs( Z=-5.189, P<0.001), polyp blood flow signal(χ 2=33.630, P<0.001), vascular stalk width(Z=-7.366, P<0.001), polyp enhancement time(χ 2=22.487, P<0.001), enhancement intensity in arterial phase(χ 2=44.371, P<0.001), polyp vascular morphology(χ 2=53.814, P<0.001)and gallbladder wall integrity(χ 2=13.277, P=0.001).The sensitivity, specificity and accuracy of CEUS in distinguishing gallbladder adenomas from cholesterol polyps were 85.37%, 89.47% and 88.24%, respectively, and the area under the curve was 0.874. Conclusion CEUS can effectively distinguish gallbladder adenomas from cholesterol polyps and help patients with GPLs to select the appropriate treatment.

Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks.

Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs. CEIs: sensitivity, 94.9% vs. 98.2%; specificity, 93.9% vs. 85.8%; PPV, 92.5% vs. 81.7%; NPV, 93.5% vs. 99.9%; and overall accuracy, 91.5% vs. 90.1%). The ResNet model is a powerful tool that can be used for AI-based accurate diagnosis of colorectal polyps.

Optical diagnosis of colorectal polyps using novel blue light imaging classification among trainee endoscopists.

Blue light imaging (BLI) has been shown to improve the characterization of colorectal polyps among the endoscopy experts. We aimed to determine if this technology could be taught to endoscopy trainees while maintaining high accuracy and interobserver agreement. Twenty-one gastroenterology trainees (fellows) from two academic institutions participated in this prospective study. Each trainee completed a web-based learning comprising four modules: pre-test, didactic videos explaining the BLI Adenoma Serrated International Classification (BASIC), interactive examples, and post-test assessment. The pre- and post-test modules consisted of reviewing video images of colon polyps in high definition white light imaging and BLI and then applying the BASIC classification to determine if the polyps were likely to be adenomatous. Confidence in adenoma identification (rated '1' to '5'), accuracy in polyp (adenoma vs. non-adenoma) identification, and agreement in characterization per BASIC criteria were derived. Trainee accuracy in the adenoma diagnosis improved from 74.7% (pre-test) to 85.4% (post-test) (P < 0.01). There was a trend towards higher accuracy in polyp characterization with subsequent years of training (1st year fellows 77.4%, 2nd year 88.5%, and final year 94.0%) with consistent improvements after the e-learning across years of trainees. Overall, trainees were able to identify adenoma with a high sensitivity of 86.9%, specificity 83.9%, positive predictive value of 84.4%, and negative predictive value of 86.5%. However, their interobserver agreement in adenoma diagnosis was moderate (k = 0.52). The novel BLI classification can be easily taught to gastroenterology trainees using an online module and accuracy improves with years of training reaching >90% for colorectal polyp characterization.

Automatic polyp detection and segmentation using shuffle efficient channel attention network

Colorectal cancer (CRC) represents one of the common malignancies of the gastrointestinal tract. The CRC incidence and mortality rates can be significantly reduced through early detection and resection of the precursor lesions, also known the colorectal polyps. However, such polyps can be missed during manual colonoscopy screening. With recent advances in artificial intelligence, numerous computer-aided diagnosis (CAD) methods have been proposed for colonoscopy applications. In particular, deep learning algorithms have been recently designed to incorporate sophisticated attention mechanisms into convolutional blocks and hence demonstrate a great potential for enhancing the performance of convolutional neural networks (CNNs). Nevertheless, most current deep learning techniques suffer from the high model complexity and excessive computational burden. In this paper, we introduce a deep learning approach for colorectal polyp detection and segmentation. Specifically, we propose a new shuffle efficient channel attention network (sECANet) with no dimensionality reduction. This network can be exploited to learn effective channel attention by obtaining cross-channel interactions. A total of 2112 manually-labeled images were collected from 1197 patients in a local hospital using colonoscopy screening. Additional data samples were collected from the CVC-ClinicDB, the ETIS-Larib Polyp DB and the Kvasir-SEG dataset. The captured images were partitioned into 3590 training images and 330 testing images, and each image was labeled as a polyp or non-polyp image. We assessed our framework on the testing images and achieved a precision of 94.9%, a recall of 96.9%, a F1 score of 95.9%, and a F2 score of 96.5%. In conclusion, our proposed framework has a great potential of assisting endoscopists in tracking polyps during colonoscopy and therefore performing early and timely resection of such polyps before they evolve into invasive cancer types.

The Role of Endoscopic Ultrasonography in the Differential Diagnosis of Polypoid Lesions of the Gallbladder: an Update

Although most gallbladder (GB) polyps are benign, some early carcinomas of the GB share the same appearance as benign polyps. Currently, GB polyps larger than 1 cm should be surgically removed because of the increased risk of malignancy. Distinguishing between nonneoplastic and neoplastic lesions is are a major diagnostic dilemma, and the therapeutic options for these lesions remain controversial. Endoscopic ultrasonography (EUS) is considered to be superior to the conventional US for imaging GB lesions since EUS can provide high-resolution images of small lesions with higher ultrasound frequencies. However, differential diagnosis remains difficult, especially for small GB polyps. Thus, various diagnostic methods using EUS have been introduced to overcome the difficulty of differential diagnosis between neoplastic and non-neoplastic polyps. Contrast-enhanced harmonic EUS is useful for observing microvascular patterns and additional enhancement images of GB polyps. This requires a post-recording analysis due to a short enhancement time. Real-time color Doppler-EUS provides the Doppler flow of vessels without a time limit. Keywords: Gallbladder, polyp; Endoscopic ultrasonography 중심 단어: ë‹´ë‚­ìš©ì¢ ; 내시경 초음파

Occurrence of the azooxanthellate coral Phyllangia pequegnatae (Scleractinia: Caryophylliidae) in the Colombian Caribbean Sea: hidden diversity or another invasive species?

As part of a baseline biodiversity study of the marine fauna associated with the Marina of Puerto Velero (PVM) in the Colombian Caribbean, we record the presence of the azooxanthellate coral Phyllangia pequegnatae Cairns, 2000. This new record extends the geographic distribution of this species to the southwestern Caribbean and increases its bathymetric range to the shallow 0.3 m depth as this species was previously known between 48 and 112 m depth. The colony of P. pequegnatae consists of 15 corallites and it was collected from underneath one of the main docks (concrete). Images of live extended polyps and detailed corallite morphology illustrate the differences between the recorded species P. pequegnatae with respect to its congeneric Phyllangia americana americana Milne Edwards & Haime, 1849. We discussed two hypothetical scenarios for the occurrence of P. pequegnatae in the Colombian Caribbean: the expansion of the bathymetric range of a previously overlooked native species or its introduction via the fouling community of small vessels.

An improved framework for polyp image segmentation based on SegNet architecture

AbstractA polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir‐SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polyp image. This paper has demonstrated state‐of‐the‐art performance on both VGG‐16, and VGG‐19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG‐19 model has outperformed the VGG‐16 model via all evaluation parameters except sensitivity for the polyp segmentation on the Kvasir‐SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time.

New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

While colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.