SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis.

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to unseen datasets that hinder clinical translation. Endoscopic imaging data is characterised by large inter- and intra-patient variability that makes these models more challenging to learn representative features for downstream tasks. Thus, despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin within the cosine similarity metrics. Our novel approach enables models to learn to cluster similar representations, thereby improving their ability to provide better separation between different classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets. We evaluated our approach for classification, detection, and segmentation. SSL-CPCD attains notable Top 1 accuracy of 79.77% in ulcerative colitis classification, an 88.62% mean average precision (mAP) for detection, and an 82.32% dice similarity coefficient for segmentation tasks. These represent improvements of over 4%, 2%, and 3%, respectively, compared to the baseline architectures. We demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting over 7% improvement.

Real-time surgical tool detection with multi-scale positional encoding and contrastive learning.

Real-time detection of surgical tools in laparoscopic data plays a vital role in understanding surgical procedures, evaluating the performance of trainees, facilitating learning, and ultimately supporting the autonomy of robotic systems. Existing detection methods for surgical data need to improve processing speed and high prediction accuracy. Most methods rely on anchors or region proposals, limiting their adaptability to variations in tool appearance and leading to sub-optimal detection results. Moreover, using non-anchor-based detectors to alleviate this problem has been partially explored without remarkable results. An anchor-free architecture based on a transformer that allows real-time tool detection is introduced. The proposal is to utilize multi-scale features within the feature extraction layer and at the transformer-based detection architecture through positional encoding that can refine and capture context-aware and structural information of different-sized tools. Furthermore, a supervised contrastive loss is introduced to optimize representations of object embeddings, resulting in improved feed-forward network performances for classifying localized bounding boxes. The strategy demonstrates superiority to state-of-the-art (SOTA) methods. Compared to the most accurate existing SOTA (DSSS) method, the approach has an improvement of nearly 4% on mAP and a reduction in the inference time by 113%. It also showed a 7% higher mAP than the baseline model.

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

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

A multi-centre polyp detection and segmentation dataset for generalisability assessment.

Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation.

The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.

Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.

Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.

Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion.

Precise instrument segmentation aids surgeons to navigate the body more easily and increases patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to: (1) a complex surgical environment, and (2) model design trade-off in terms of both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 challenge (ROBUST-MIS) provides more than 10,000 frames with surgical tools in different clinical settings. In this paper, we propose a light-weight single stage instance segmentation model complemented with a convolutional block attention module for achieving both faster and accurate inference. We further improve accuracy through data augmentation and optimal anchor localization strategies. To our knowledge, this is the first work that explicitly focuses on both real-time performance and improved accuracy. Our approach out-performed top team performances in the most recent edition of ROBUST-MIS challenge with over 44% improvement on area-based multi-instance dice metric MI_DSC and 39% on distance-based multi-instance normalized surface dice MI_NSD. We also demonstrate real-time performance (>60 frames-per-second) with different but competitive variants of our final approach.

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy.

Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment of kidney stones. Automated segmentation of kidney stones and the laser fiber is an essential initial step to performing any automated quantitative analysis, particularly stone-size estimation, that can be used by the surgeon to decide if the stone requires further fragmentation. However, factors such as turbid fluid inside the cavity, specularities, motion blur due to kidney movements and camera motion, bleeding, and stone debris impact the quality of vision within the kidney, leading to extended operative times. To the best of our knowledge, this is the first attempt made towards multi-class segmentation in ureteroscopy and laser lithotripsy data. We propose an end-to-end convolution neural network (CNN) based learning framework for the segmentation of stones and laser fiber. The proposed approach utilizes two sub-networks: (I) HybResUNet, a hybrid version of residual U-Net, that uses residual connections in the encoder path of the U-Net to improve semantic predictions, and (II) a DVFNet that generates deformation vector field (DVF) predictions by leveraging motion differences between the adjacent video frames which is then used to prune the prediction maps. We also present ablation studies that combine different dilated convolutions, recurrent and residual connections, atrous spatial pyramid pooling, and attention gate models. Further, we propose a compound loss function that significantly boosts the segmentation performance in our data. We have also provided an ablation study to determine the optimal data augmentation strategy for our dataset. Our qualitative and quantitative results illustrate that our proposed method outperforms state-of-the-art methods such as UNet and DeepLabv3+ showing a DSC improvement of 4.15% and 13.34%, respectively, in our in vivo test dataset. We further show that our proposed model outperforms state-of-the-art methods on an unseen out-of-sample clinical dataset with a DSC improvement of 9.61%, 11%, and 5.24% over UNet, HybResUNet, and DeepLabv3+, respectively in the case of the stone class and an improvement of 31.79%, 22.15%, and 10.42% over UNet, HybResUNet, and DeepLabv3+, respectively, in case of the laser class.

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation.

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%-4% in dice score compared to its counterpart MAML for most experiments.

MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation.

Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting-edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests and achieved DSC of 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.

TGANet: Text-guided attention for improved polyp segmentation.

Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps. In this work, we exploit size-related and polyp number-related features in the form of text attention during training. We introduce an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps. Our experimental results demonstrate that these added text embeddings improve the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed text-guided attention network (TGANet) can generalize well to variable-sized polyps in different datasets. Codes are available at https://github.com/nikhilroxtomar/TGANet.

Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures.

Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and high-quality datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time, running at 5 frames-per-second (fps) at most. However, for the method to be clinically applicable, a real-time capability is utmost required along with high accuracy. In this paper, we propose the addition of attention mechanisms to the YOLACT architecture to allow real-time instance segmentation of instruments with improved accuracy on the ROBUST-MIS dataset. Our proposed approach achieves competitive performance compared to the winner of the 2019 ROBUST-MIS challenge in terms of robustness scores, obtaining 0.313 ML_DSC and 0.338 MLNSD while reaching real-time performance at >45 fps.

A Pilot Study on Automatic Three-Dimensional Quantification of Barrett's Esophagus for Risk Stratification and Therapy Monitoring.

BACKGROUND & AIMS: Barrett's epithelium measurement using widely accepted Prague C&M classification is highly operator dependent. We propose a novel methodology for measuring this risk score automatically. The method also enables quantification of the area of Barrett's epithelium (BEA) and islands, which was not possible before. Furthermore, it allows 3-dimensional (3D) reconstruction of the esophageal surface, enabling interactive 3D visualization. We aimed to assess the accuracy of the proposed artificial intelligence system on both phantom and endoscopic patient data. METHODS: Using advanced deep learning, a depth estimator network is used to predict endoscope camera distance from the gastric folds. By segmenting BEA and gastroesophageal junction and projecting them to the estimated mm distances, we measure C&M scores including the BEA. The derived endoscopy artificial intelligence system was tested on a purpose-built 3D printed esophagus phantom with varying BEAs and on 194 high-definition videos from 131 patients with C&M values scored by expert endoscopists. RESULTS: Endoscopic phantom video data demonstrated a 97.2% accuracy with a marginal ± 0.9 mm average deviation for C&M and island measurements, while for BEA we achieved 98.4% accuracy with only ±0.4 cm2 average deviation compared with ground-truth. On patient data, the C&M measurements provided by our system concurred with expert scores with marginal overall relative error (mean difference) of 8% (3.6 mm) and 7% (2.8 mm) for C and M scores, respectively. CONCLUSIONS: The proposed methodology automatically extracts Prague C&M scores with high accuracy. Quantification and 3D reconstruction of the entire Barrett's area provides new opportunities for risk stratification and assessment of therapy response.

Spectral Endoscopy Enhances Contrast for Neoplasia in Surveillance of Barrett's Esophagus.

Early detection of esophageal neoplasia enables curative endoscopic therapy, but the current diagnostic standard of care has low sensitivity because early neoplasia is often inconspicuous with conventional white-light endoscopy. Here, we hypothesized that spectral endoscopy could enhance contrast for neoplasia in surveillance of patients with Barrett's esophagus. A custom spectral endoscope was deployed in a pilot clinical study of 20 patients to capture 715 in vivo tissue spectra matched with gold standard diagnosis from histopathology. Spectral endoscopy was sensitive to changes in neovascularization during the progression of disease; both non-dysplastic and neoplastic Barrett's esophagus showed higher blood volume relative to healthy squamous tissue (P = 0.001 and 0.02, respectively), and vessel radius appeared larger in neoplasia relative to non-dysplastic Barrett's esophagus (P = 0.06). We further developed a deep learning algorithm capable of classifying spectra of neoplasia versus non-dysplastic Barrett's esophagus with high accuracy (84.8% accuracy, 83.7% sensitivity, 85.5% specificity, 78.3% positive predictive value, and 89.4% negative predictive value). Exploiting the newly acquired library of labeled spectra to model custom color filter sets identified a potential 12-fold enhancement in contrast between neoplasia and non-dysplastic Barrett's esophagus using application-specific color filters compared with standard-of-care white-light imaging (perceptible color difference = 32.4 and 2.7, respectively). This work demonstrates the potential of endoscopic spectral imaging to extract vascular properties in Barrett's esophagus, to classify disease stages using deep learning, and to enable high-contrast endoscopy. SIGNIFICANCE: The results of this pilot first-in-human clinical trial demonstrate the potential of spectral endoscopy to reveal disease-associated vascular changes and to provide high-contrast delineation of neoplasia in the esophagus. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/12/3415/F1.large.jpg.

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

Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques.

A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging.

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.

A deep learning framework for quality assessment and restoration in video endoscopy.

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. In this paper, a fully automatic framework is proposed that can: 1) detect and classify six different artifacts, 2) segment artifact instances that have indefinable shapes, 3) provide a quality score for each frame, and 4) restore partially corrupted frames. To detect and classify different artifacts, the proposed framework exploits fast, multi-scale and single stage convolution neural network detector. In addition, we use an encoder-decoder model for pixel-wise segmentation of irregular shaped artifacts. A quality score is introduced to assess video frame quality and to predict image restoration success. Generative adversarial networks with carefully chosen regularization and training strategies for discriminator-generator networks are finally used to restore corrupted frames. The detector yields the highest mean average precision (mAP) of 45.7 and 34.7, respectively for 25% and 50% IoU thresholds, and the lowest computational time of 88 ms allowing for near real-time processing. The restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos, an average of 68.7% of video frames successfully passed the quality score (≥0.9) after applying the proposed restoration framework thereby retaining 25% more frames compared to the raw videos. The importance of artifacts detection and their restoration on improved robustness of image analysis methods is also demonstrated in this work.

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods.

Clinical evaluation of a device providing simultaneous white-light and fluorescence video streams as well as panoramic imaging during fluorescence assisted-transurethral resection of bladder cancer.

The current clinical study is aimed at evaluating the clinical relevance of an innovative device (called CyPaM2 device) that for the first time provides urologists with (i) a panoramic image of the bladder inner wall within the surgery time, and with (ii) a simultaneous (bimodal) display of fluorescence and white-light video streams during the fluorescence assisted-transurethral resection of bladder cancers procedure. The clinical relevance of this CyPaM2 device was evaluated on 10 patients according to three criteria (image quality, fluorescent lesions detection relevance, and ergonomics) compared with a reference medical device. Innovative features displayed by the CyPaM2 device were evaluated without any possible comparison: (i) simultaneous bimodal display of white-light and fluorescence video streams, (ii) remote light control, and (iii) time delay for the panoramic image building. The results highlight the progress to achieve in order to obtain a fully mature device ready for commercialization and the relevance of the innovative features proposed by the CyPaM2 device confirming their interest.