Endoscopic Image Quality Research Articles

Synchronizing Detection and Removal of Smoke in Endoscopic Images With Cyclic Consistency Adversarial Nets.

Smoke removal is an important and meaningful issue for endoscopic surgery, which can enhance the visual quality of endoscopic images. Because it is practically impossible to construct a large training dataset of pair-matched endoscopic images with/without smoke, the Generative Adversarial Nets (GANs) based models are usually used for endoscopic image desmoke. But they have difficulties in either locating the accurate smoke area, or recovering realistic internal organ or tissue details. In this paper, we propose a new approach, called Desmoke-CycleGAN, which combined detection, estimation, and removal of smoke together, to improve the CycleGAN model for endoscopic image smoke removal. In addition, both pixel-level and perceptual-level consistency loss have been incorporated in the proposed model, which helps the model to be more stable and efficient for recovering realistic details in endoscopic images. The experimental results have demonstrated that this method outperforms other state-of-the-art smoke removal approaches with unpaired real endoscopic images.

P489 Systematic review and meta-analysis of the accuracy of computer-aided diagnosis in identifying endoscopic remission in Ulcerative Colitis

Abstract Background Endoscopic remission (ER), the absence of active inflammation during endoscopy, is a key therapeutic target in Ulcerative Colitis (UC). ER is assessed during colonoscopy using standardised scoring systems. However, there remains significant inter-operator variability in assessing ER, for which the application of computer-aided diagnosis (CADx) could be a potential solution. A previous systematic review and meta-analysis on this topic was limited to literature up to June 2022 and did not assess the methodological quality of studies or heterogeneity amongst results. We aimed to update this review. Methods Searches were performed on six electronic databases and relevant reference lists up to May 2023. Data was extracted and screened independently by two reviewers. Methodological assessment was performed using the QUADAS-2 tool. An ‘image selection’ domain was added to the tool given the importance of endoscopic image quality in training and testing data on CADx’s accuracy. Meta-analysis was performed using a bivariate model. Heterogeneity was assessed through visual inspection of forest plots and receiver-operating characteristic curves, subgroup analyses and meta-regression. The overall quality of evidence was assessed using GRADE methodology. Results 17 studies (16 full-text articles and one abstract) were identified. 15 studies adopted a cross-sectional design. All studies used expert endoscopists scoring of ER as the reference standard. The Mayo Endoscopic Score was the most commonly used scoring system (13/17). CADx assessed ER in real-time in one study, but was applied to images and videos obtained from previous endoscopic procedures in all other studies. Inception-v3 was the most common CADx system used (6/17). ‘Patient selection’ was the domain of greatest concern due to a considerable number of studies using images from the same public database and under-reporting participant characteristics. All studies had a low risk of bias (ROB) in the ‘image selection’ domain. One study was deemed overall high ROB, and two were deemed overall unclear ROB. Sensitivity analysis was performed by removing these studies and the abstract, and pooled estimates were obtained from the remaining 13 studies. CADx had a pooled sensitivity of 90.3% and specificity of 94.6% for diagnosing ER. The overall quality of evidence was moderate due to the failure of a significant proportion of studies to report sufficient participant characteristics. Conclusion There is moderate quality evidence that CADx has high sensitivity and specificity for diagnosing ER in pre-clinical studies. Further evaluation is required in randomised controlled trials to evaluate its efficacy in real-time and human-computer interactions.

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach.

Endoscopies are helpful for examining internal organs, including the gastrointestinal tract. The endoscope device consists of a flexible tube to which a camera and light source are attached. The diagnostic process heavily depends on the quality of the endoscopic images. That is why the visual quality of endoscopic images has a significant effect on patient care, medical decision-making, and the efficiency of endoscopic treatments. In this study, we propose an endoscopic image enhancement technique based on image fusion. Our method aims to improve the visual quality of endoscopic images by first generating multiple sub images from the single input image which are complementary to one another in terms of local and global contrast. Then, each sub layer is subjected to a novel wavelet transform and guided filter-based decomposition technique. To generate the final improved image, appropriate fusion rules are utilized at the end. A set of upper gastrointestinal tract endoscopic images were put to the test in studies to confirm the efficacy of our strategy. Both qualitative and quantitative analyses show that the proposed framework performs better than some of the state-of-the-art algorithms.

Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images.

Endoscopy is a commonly used clinical method for gastrointestinal disorders. However, the complexity of the gastrointestinal environment can lead to artifacts. Consequently, the artifacts affect the visual perception of images captured during endoscopic examinations. Existing methods to assess image quality with no reference display limitations: some are artifact-specific, while others are poorly interpretable. This study presents an improved cascade region-based convolutional neural network (CNN) for detecting gastrointestinal artifacts to quantitatively assess the quality of endoscopic images. This method detects eight artifacts in endoscopic images and provides their localization, classification, and confidence scores; these scores represent image quality assessment results. The artifact detection component of this method enhances the feature pyramid structure, incorporates the channel attention mechanism into the feature extraction process, and combines shallow and deep features to improve the utilization of spatial information. The detection results are further used for image quality assessment. Experimental results using white light imaging, narrow-band imaging, and iodine-stained images demonstrate that the proposed artifact detection method achieved the highest average precision (62.4% at a 50% IOU threshold). Compared to the typical networks, the accuracy of this algorithm is improved. Furthermore, three clinicians validated that the proposed image quality assessment method based on the object detection of endoscopy artifacts achieves a correlation coefficient of 60.71%.

The Role of Cholangioscopy in Biliary Diseases.

Endoscopy plays a central role in diagnostic and therapeutic approaches to biliary disease in both benign and malignant conditions. A cholangioscope is an endoscopic instrument that allows for the direct exploration of the biliary tree. Over the years, technology has improved endoscopic image quality and allowed for the development of an operative procedure that can be performed during cholangioscopy. Different types of instruments are available in this context, and they can be used in different anatomical access points according to the most appropriate clinical indication. The direct visualization of biliary mucosa is essential in the presence of biliary strictures of unknown significance, allowing for the appropriate allocation of patients to surgery or conservative treatments. Cholangioscopy has demonstrated excellent performance in discriminating malignant conditions (such as colangiocarcinoma) from benign inflammatory strictures, and more recent advances (e.g., artificial intelligence and confocal laser endomicroscopy) could further increase its diagnostic accuracy. Cholangioscopy also plays a primary role in the treatment of benign conditions such as difficult bile stones (DBSs). In this case, it may not be possible to achieve complete biliary drainage using standard ERCP. Therapeutic cholangioscopy-guided lithotripsy allows for stone fragmentation and complete biliary drainage. Indeed, other complex clinical situations, such as patients with intra-hepatic lithiasis and patients with an altered anatomy, could benefit from the therapeutic role of cholangioscopy. The aim of the present review is to explore the most recent diagnostic and therapeutic advances in the roles of cholangioscopy in the management of biliary diseases.

Improving Endoscopic Image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance.

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet

Endoscopic image has complex backgrounds and spatially different noise, bringing mainstream denoising methods to the problem of incomplete noise removal and the loss of image detail. Thus, an endoscopic image denoising algorithm based on spatial attention UNet network is proposed in this paper. UNet based on residual learning is utilized as the backbone network. Spatial attention modules based on noise intensity estimation and edge feature extraction modules are used to remove noise better while preserving the image details and improving generalization ability. We take endoscopic images of real scenes using gastroscopy and compare our method with mainstream methods. Experimental results show that our approach improves PSNR by 3.51 or 2.93 and SSIM by 0.03 or 0.015 compared with CBDNet or EDCNN, respectively. Our method can effectively improve the impact of noise on the image quality of endoscopic images, thus better assisting doctors in diagnosis and treatment.

A 3D reconstruction based on an unsupervised domain adaptive for binocular endoscopy.

In minimally invasive surgery, endoscopic image quality plays a crucial role in surgery. Aiming at the lack of a real parallax in binocular endoscopic images, this article proposes an unsupervised adaptive neural network. The network combines adaptive smoke removal, depth estimation of binocular endoscopic images, and the 3D display of high-quality endoscopic images. We simulated the smoke generated during surgery by artificially adding fog. The training images of U-Net fused by Laplacian pyramid are introduced to improve the network’s ability to extract intermediate features. We introduce Convolutional Block Attention Module to obtain the optimal parameters of each layer of the network. We utilized the disparity transformation relationship between left- and right-eye images to combine the left-eye images with disparity in HS-Resnet to obtain virtual right-eye images as labels for self-supervised training. This method extracts and fuses the parallax images at different scale levels of the decoder, making the generated parallax images more complete and smoother. A large number of experimental research results show that the scheme can remove the smoke generated during the operation, effectively reconstruct the 3D image of the tissue structure of the binocular endoscope, and at the same time, preserve the contour, edge, detail, and texture of the blood vessels in the medical image. Compared with the existing similar schemes, various indicators have been greatly improved. It has good clinical application prospects.

An Endoscope Image Enhancement Algorithm Based on Image Decomposition

The visual quality of endoscopic images is a significant factor in early lesion inspection and surgical procedures. However, due to the interference of light sources, hardware, and other configurations, the endoscopic images collected clinically have uneven illumination, blurred details, and contrast. This paper proposed a new endoscopic image enhancement algorithm. The image decomposes into a detail layer and a base layer based on noise suppression. The blood vessel information is stretched by channel in the detail layer, and adaptive brightness correction is performed in the base layer. Finally, Fusion obtained a new endoscopic image. This paper compares the algorithm with six other algorithms in the laboratory dataset. The algorithm is in the leading position in all five objective evaluation metrics, further indicating that the algorithm is ahead of other algorithms in contrast, structural similarity, and peak signal-to-noise ratio. It can effectively highlight the blood vessel information in endoscopic images while avoiding the influence of noise and highlight points. The proposed algorithm can well solve the existing problems of endoscopic images.

A Medical Endoscope Image Enhancement Method Based on Improved Weighted Guided Filtering

In clinical surgery, the quality of endoscopic images is degraded by noise. Blood, illumination changes, specular reflection, smoke, and other factors contribute to noise, which reduces the quality of an image in an occluded area, affects doctors’ judgment, prolongs the operation duration, and increases the operation risk. In this study, we proposed an improved weighted guided filtering algorithm to enhance endoscopic image tissue. An unsharp mask algorithm and an improved weighted guided filter were used to enhance vessel details and contours in endoscopic images. The scheme of the entire endoscopic image processing, which included detail enhancement, contrast enhancement, brightness enhancement, and highlight area removal, is presented. Compared with other algorithms, the proposed algorithm maintained edges and reduced halos efficiently, and its effectiveness was demonstrated using experiments. The peak signal-to-noise ratio and structural similarity of endoscopic images obtained using the proposed algorithm were the highest. The foreground–background detail variance–background variance improved. The proposed algorithm had a strong ability to suppress noise and could maintain the structure of original endoscopic images, which improved the details of tissue blood vessels. The findings of this study can provide guidelines for developing endoscopy devices.

Diagnostic Accuracy of H. pylori Status by Conventional Endoscopy: Time-Trend Change After Eradication and Impact of Endoscopic Image Quality.

AimTo assess the time trend of diagnostic accuracy of pre- and post-eradication H. pylori status and interobserver agreement of gastric atrophy grading.MethodsA series 100 of conventional endoscopic image sets taken from subjects undergoing gastric cancer screening at a polyclinic were evaluated by 5 experienced assessors. Each assessor independently examined endoscopic findings according to the Kyoto classification and then determined the H. pylori status (never, current, or past infected). Gastric atrophy was assessed according to the Kimura-Takemoto classification and classified into 3 grades (none/mild, moderate, or severe). The image series that ≥3 assessors considered to have good quality were arbitrarily defined as high-quality image (HQI) series, and the rest were defined as low-quality image (LQI) series.ResultsThe overall diagnostic accuracy of H. pylori status was 83.0%. It was lowest in subjects with current infection (54%), gradually increased at 1 year (79%, P < 0.001) and 3 years (94.0%, P = 0.002), but then did not significantly change at 5 years (91.0%, P = 0.420) after eradication. The rate of LQI series was 28%. The overall diagnostic accuracy of H. pylori status dropped from 88.9% to 67.9% (P < 0.001), and the mean kappa value on gastric atrophy grading dropped from 0.730 to 0.580 (P = 0.002) in the HQI and LQI series, respectively.ConclusionsDiagnostic accuracy of H. pylori status increased over time after eradication. LQI series badly affected the diagnostic accuracy of H. pylori status and the level of agreement when grading gastric atrophy.

Cycle-Consistent Adversarial Networks for Smoke Detection and Removal in Endoscopic Images.

During endoscopic surgery, smoke removal is important and meaningful for increasing the visual quality of endoscopic images. However, unlike natural image dehaze, it is practical impossible to build a large paired endoscopic image training dataset with/without smoke. Therefore, in this paper, we propose a new approach, called Desmoke-CycleGAN, which combined detection and removal of smoke together, to improve the CycleGAN model for endoscopic image smoke removal. The detector can provide information about smoke locations and densities, which helps the GAN model to be more stable and efficient for smoke removal. Although some imperfections still exist, the experimental results have demonstrated that this method outperforms other state-of-the-art smoke removal approaches with unpaired real endoscopic images.Clinical Relevance- This can help improve the visibility in endoscopic surgery and to get smoke-free endoscopic images with better quality.

Possibility of new shielding device for upper gastrointestinal endoscopy.

Background and study aims Infection control is essential when performing endoscopic procedures, especially during the COVID-19 pandemic. Therefore, we have developed a new shielding device called STEP for infection control in upper gastrointestinal endoscopy. Patients and methods STEP consists of a mask worn by the patient and a drape that is connected to the mask and covers the endoscope. A suction tube attached to the mask prevents aerosols from spreading. The endoscopist operates the endoscope through the drape. Three endoscopists performed a total of 18 examinations using an upper endoscopy training model with and without STEP. Endoscopic images were evaluated by three other endoscopists, using a visual analog scale. We also simulated contact, droplet, and aerosol infection and evaluated the utility of STEP. Results All examinations were conducted without a problem. Mean procedure time was 126.3 ± 11.6 seconds with STEP and 122.3 ± 10.0 seconds without STEP. The mean visual analog score was 90.7 ± 10.1 with STEP and 90.4 ± 10.0 without STEP. In the contact model, adherence of simulated contaminants was 4.9 ± 1.4 % without STEP and 0 % with STEP. In the droplet model, the number of simulated contaminants attached to the paper was 338 273 ± 90 735 pixels without STEP and 0 with STEP. In the aerosol model, the total number of particles was 346 837 ± 9485 without STEP and was significantly reduced to 222 ± 174 with STEP. Conclusions No effect on examination time or endoscopic image quality was observed when using STEP in upper gastrointestinal endoscopy. Using STEP reduced the diffusion of simulated contaminants in all three infection models.

A desmoking algorithm for endoscopic images based on improved U‐Net model

AbstractIn laparoscopic surgery, the smoke generated by operations including electrocautery and laser ablation seriously degrades the quality of endoscopic images. It not only reduces the visibility of the surgery, leading to increased risk of surgery, but also affects the performance of image processing in computer‐assisted surgery such as segmentation, 3D reconstruction and tracking. Therefore, a desmoking algorithm is required to eliminate smoke in endoscopic images. In this article, we study a U‐Net model that can eliminate smoke of the laparoscopic image in real‐time and preserve the natural appearance of the organ surface. Our method is based on the improved U‐Net in which convolutional block attention module is used as an embedded guide mask of the decoder part. The laparoscopic image dataset is provided by the Hamlyn Center, and the Blender software is used to simulate various situations of smoke added to the laparoscopic image for training and testing. For the proposed method, the peak signal‐to‐noise ratio value is up to 29.27 and the structural similarity index is up to 0.945 over the test images. The experimental results demonstrate that the proposed method achieves a better performance than other six existing methods, which is applicable for real‐time endoscopic image processing.

Skill-up study of systemic endoscopic examination technique using narrow band imaging of the head and neck region of patients with esophageal squamous cell carcinoma: Prospective multicenter study.

To evaluate the usefulness of a training program on endoscopic head and neck surveillance for beginner endoscopists. This prospective multicenter study included 13 beginner endoscopists from 10 institutions who received training in systematic observation techniques and diagnostic criteria, and the training involved hands-on learning. Between May 2016 and February 2017, enrolled patients with current or previously diagnosed esophageal squamous cell carcinomas underwent head and neck surveillance using narrow band imaging (NBI) endoscopy, and histologically confirmed head and neck squamous cell carcinoma (HNSCC) detection rates, endoscopic image quality, and examination times were compared before (group A) and after (group B) the training program. Maximum possible score for the endoscopic images was 30 points. A total of 330 patients, comprising 181 in group A and 149 in group B, were enrolled. Three patients with HNSCC were detected in group A (1.7%) and in group B (2.0%; P=1.000). Mean±standard deviation (SD) examination times were 157±71 s and 174±109s in groups A and B, respectively, (P=0.073). Mean±SD scores of the endoscopic images were 25.04±5.47points and 27.01±4.35points in groups A and B, respectively, (P<0.001). The HNSCC detection rate based on the use of NBI on patients with ESCC did not improve after the training program for beginner endoscopists; however, endoscopic image quality improved significantly after the training program.

A vascular enhancement algorithm for endoscope image

Endoscopic image quality plays an important role in the diagnosis of early lesions and dysplasia. Therefore, a blood vessel enhancement algorithm based on spectral absorption characteristics of blood vessels is proposed in this paper. First of all, RGB channels are obtained from the color image and divided into the brightness layer with the high dynamic range and the detail layer with the detail image information through the guided filter. Then, the detail layer of each channel is adaptively enhanced based on SNR (signal noise ratio), and the brightness layer is stretched to improve the GB channel information and to reduce R channel information. Finally each channel is merged to generate an enhanced image. In this article, a large number of endoscopic images is applied to this algorithm and compared with Karl Stroz's Spectra B enhancement technology. This method performs better in image enhancements while using the Detail Variance-Background Variance index and the Weber contrast index to evaluate.

Efficient Enhancement of Stereo Endoscopic Images Based on Joint Wavelet Decomposition and Binocular Combination.

The success of minimally invasive interventions and the remarkable technological and medical progress have made endoscopic image enhancement a very active research field. Due to the intrinsic endoscopic domain characteristics and the surgical exercise, stereo endoscopic images may suffer from different degradations which affect its quality. Therefore, in order to provide the surgeons with a better visual feedback and improve the outcomes of possible subsequent processing steps, namely, a 3-D organ reconstruction/registration, it would be interesting to improve the stereo endoscopic image quality. To this end, we propose, in this paper, two joint enhancement methods which operate in the wavelet transform domain. More precisely, by resorting to a joint wavelet decomposition, the wavelet subbands of the right and left views are simultaneously processed to exploit the binocular vision properties. While the first proposed technique combines only the approximation subbands of both views, the second method combines all the wavelet subbands yielding an inter-view processing fully adapted to the local features of the stereo endoscopic images. Experimental results, carried out on various stereo endoscopic datasets, have demonstrated the efficiency of the proposed enhancement methods in terms of perceived visual image quality.

Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images.

Modern endoscopes play a significant role in diagnosing various gastrointestinal (GI) tract related diseases where the visual quality of endoscopic images helps improving the diagnosis. This article presents an image enhancement method for color endoscopic images that consists of three stages, and hence termed as "Tri-scan" enhancement: (1) tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics; (2) mucosa layer enhancement: an adaptive sigmoid function is employed on the R plane of the image to highlight micro-vessels of the superficial layers of the mucosa and submucosa; and (3) color tone enhancement: the pixels are uniformly distributed to create an enhanced color effect to highlight the subtle micro-vessels, mucosa and tissue characteristics. The proposed method is used on a large data set of low contrast color white light images (WLI). The results are compared with three existing enhancement techniques: Narrow Band Imaging (NBI), Fuji Intelligent Color Enhancement (FICE) and i-scan Technology. The focus value and color enhancement factor show that the enhancement level achieved in the processed images is higher compared to NBI, FICE and i-scan images.

A randomized controlled trial of improvement of endoscopic visibility in the upper gastrointestinal tract with N-acetylcysteine

Objective To determine the effects and dosage of N-acetylcysteine(NAC) in the improvement of the visibility in esophagogastroduodenoscopy(EGD). Methods A prospective randomized controlled study was performed on 193 patients scheduled for EGD from November 2014 to July 2015 were randomized into five groups using digital table. In group A, 100 mg dimethicone and 2 g NaHCO3 were given. In group B, 100 mg dimethicone, 2 g NaHCO3 and 20 000 U pronase were given. Group C received 100 mg dimethicone, 2 g NaHCO3 and 200 mg NAC. Group D received 100 mg dimethicone, 2 g NaHCO3 and 400 mg NAC and group E 100 mg dimethicone, 2 g NaHCO3 and 600 mg NAC.The agents were dissolved in 100 ml water for each patient.Endoscopy was completed by one endoscopist and the score of image visibility assessment was completed by 2 other endoscopists. The 3 endoscopists were unaware of grouping.The total scores, the time of washing, the time of examination and complications were compared and analysed. Results The total image scores of group A, B, C, D and E were 30.83±3.78, 35.69±2.88, 33.16±3.90, 34.95±3.46 and 36.76±2.91, respectively. Group A was the lowest(P 0.05). Images that were scored 3 were the most in group E. The washing times of each group were 38.00±19.10, 17.03±11.44, 15.92±10.81, 15.78±10.24 and 15.55±9.69, and the examination times of each group were 13.49±2.49, 9.41±1.86, 9.08±1.80, 8.73±1.91 and 8.78±1.79 minutes.Group A was the longest in these two indices(P 0.05). Conclusion The preoperative NAC can improve the visibility in EGD. The best dose is 600 mg, whose effects and safety were similar to those of 20 000 U, but yield to less washing time and the examination time in EGD. Key words: Gastroscopy; Acetylcysteine; Pronase; Endoscopic image quality

Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction

Modern endoscopes play an important role in diagnosing various gastrointestinal (GI) tract related diseases. The improved visual quality of endoscopic images can provide better diagnosis. This paper presents an efficient color image enhancement method for endoscopic images. It is achieved in two stages: image enhancement at gray level followed by space variant chrominance mapping color reproduction. Image enhancement is achieved by performing adaptive sigmoid function and uniform distribution of sigmoid pixels. Secondly, a space variant chrominance mapping color reproduction is used to generate new chrominance components. The proposed method is used on low contrast color white light images (WLI) to enhance and highlight the vascular and mucosa structures of the GI tract. The method is also used to colorize grayscale narrow band images (NBI) and video frames. The focus value and color enhancement factor show that the enhancement level in the processed image is greatly increased compared to the original endoscopic image. The overall contrast level of the processed image is higher than the original image. The color similarity test has proved that the proposed method does not add any additional color which is not present in the original image. The algorithm has low complexity with an execution speed faster than other related methods.