Automatic Bleeding Frame and Region Detection for GLCM Using Artificial Neural Network

 Wireless capsule endoscopy is a device that inspects the direct visualization of patient’s gastrointestinal tract without invasiveness. Analyzing the WCE video is a time- consuming task hence computer aided technique is used to reduce the burden of medical clinicians. This paper proposes a novel color feature extraction method to detect the bleeding frame. First, we perform word based histogram for rapid bleeding detection in WCE images. Classification of bleeding WCE frame is performed by applying for glcm using  Artificial Neural Network and K-nearest neighbour method. Second we propose a two-stage saliency map extraction method. In first stage saliency, we inspect the bleeding images under different color components to highlight the bleeding regions. From second stage saliency red color in the bleeding frame reveals that the region is affected. Then, by using algorithm we fuse the two-stage of saliency to detect the bleeding area. Experimental results show that the proposed method is very efficient in detecting the bleeding frames and the region

Time-based self-supervised learning for Wireless Capsule Endoscopy

State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method that does not initially need labels or appropriate balance. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance. State-of-the-art results are achieved in polyp detection, with 95.00 ± 2.09% Area Under the Curve, and 92.77 ± 1.20% accuracy in the CAD-CAP dataset

An Overview of Image Analysis Techniques in Endoscopic Bleeding Detection

ABSTRACT:Authors review the existing bleeding detection methods focusing their attention on the image processing techniques utilised in the algorithms. In the article, 18 methods were analysed and their functional components were identified. The authors proposed six different groups, to which algorithms' components were assigned: colour techniques, reflecting features of pixels as individual values, texture techniques, considering spatial dependencies between pixels, contour techniques for edges and contours, segmentation techniques for dividing images into meaningful regions, decision mechanisms for final interpretation of the image and other techniques that do not match any of the introduced groups. Authors conclude that the algorithms could be still improved by applying more complete sets of techniques to address the importance of visual features of endoscopic bleeding. Also, improvement is possible in the area of decisive classifiers

Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy

REGION-COLOR BASED AUTOMATED BLEEDING DETECTION IN CAPSULE ENDOSCOPY VIDEOS

Capsule Endoscopy (CE) is a unique technique for facilitating non-invasive and practical visualization of the entire small intestine. It has attracted a critical mass of studies for improvements. Among numerous studies being performed in capsule endoscopy, tremendous efforts are being made in the development of software algorithms to identify clinically important frames in CE videos. This thesis presents a computer-assisted method which performs automated detection of CE video-frames that contain bleeding. Specifically, a methodology is proposed to classify the frames of CE videos into bleeding and non-bleeding frames. It is a Support Vector Machine (SVM) based supervised method which classifies the frames on the basis of color features derived from image-regions. Image-regions are characterized on the basis of statistical features. With 15 available candidate features, an exhaustive feature-selection is followed to obtain the best feature subset. The best feature-subset is the combination of features that has the highest bleeding discrimination ability as determined by the three performance-metrics: accuracy, sensitivity and specificity. Also, a ground truth label annotation method is proposed in order to partially automate delineation of bleeding regions for training of the classifier. The method produced promising results with sensitivity and specificity values up to 94%. All the experiments were performed separately for RGB and HSV color spaces. Experimental results show the combination of the mean planes in red and green planes to be the best feature-subset in RGB (Red-Green-Blue) color space and the combination of the mean values of all three planes of the color space to be the best feature-subset in HSV (Hue-Saturation-Value)

Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques

Gastrointestinal (GI) bleeding is very common in humans, which may lead to fatal consequences. GI bleeding can usually be identified using a flexible wired endoscope. In 2001, a newer diagnostic tool, wireless capsule endoscopy (WCE) was introduced. It is a swallow-able capsule-shaped device with a camera that captures thousands of color images and wirelessly sends those back to a data recorder. After that, the physicians analyze those images in order to identify any GI abnormalities. But it takes a longer screening time which may increase the danger of the patients in emergency cases. It is therefore necessary to use a real-time detection tool to identify bleeding in the GI tract. Each material has its own spectral ‘signature’ which shows distinct characteristics in specific wavelength of light [33]. Therefore, by evaluating the optical characteristics, the presence of blood can be detected. In the study, three main hardware designs were presented: one using a two-wavelength based optical sensor and others using two six-wavelength based spectral sensors with AS7262 and AS7263 chips respectively to determine the optical characteristics of the blood and non-blood samples. The goal of the research is to develop a machine learning model to differentiate blood samples (BS) and non-blood samples (NBS) by exploring their optical properties. In this experiment, 10 levels of crystallized bovine hemoglobin solutions were used as BS and 5 food colors (red, yellow, orange, tan and pink) with different concentrations totaling 25 non-blood samples were used as NBS. These blood and non-blood samples were also combined with pig’s intestine to mimic in-vivo experimental environment. The collected samples were completely separated into training and testing data. Different spectral features are analyzed to obtain the optical information about the samples. Based on the performance on the selected most significant features of the spectral wavelengths, k-nearest neighbors algorithm (k-NN) is finally chosen for the automated bleeding detection. The proposed k-NN classifier model has been able to distinguish the BS and NBS with an accuracy of 91.54% using two wavelengths features and around 89% using three combined wavelengths features in the visible and near-infrared spectral regions. The research also indicates that it is possible to deploy tiny optical detectors to detect GI bleeding in a WCE system which could eliminate the need of time-consuming image post-processing steps

Uncertainty, interpretability and dataset limitations in Deep Learning

[eng] Deep Learning (DL) has gained traction in the last years thanks to the exponential increase in compute power. New techniques and methods are published at a daily basis, and records are being set across multiple disciplines. Undeniably, DL has brought a revolution to the machine learning field and to our lives. However, not everything has been resolved and some considerations must be taken into account. For instance, obtaining uncertainty measures and bounds is still an open problem. Models should be able to capture and express the confidence they have in their decisions, and Artificial Neural Networks (ANN) are known to lack in this regard. Be it through out of distribution samples, adversarial attacks, or simply unrelated or nonsensical inputs, ANN models demonstrate an unfounded and incorrect tendency to still output high probabilities. Likewise, interpretability remains an unresolved question. Some fields not only need but rely on being able to provide human interpretations of the thought process of models. ANNs, and specially deep models trained with DL, are hard to reason about. Last but not least, there is a tendency that indicates that models are getting deeper and more complex. At the same time, to cope with the increasing number of parameters, datasets are required to be of higher quality and, usually, larger. Not all research, and even less real world applications, can keep with the increasing demands. Therefore, taking into account the previous issues, the main aim of this thesis is to provide methods and frameworks to tackle each of them. These approaches should be applicable to any suitable field and dataset, and are employed with real world datasets as proof of concept. First, we propose a method that provides interpretability with respect to the results through uncertainty measures. The model in question is capable of reasoning about the uncertainty inherent in data and leverages that information to progressively refine its outputs. In particular, the method is applied to land cover segmentation, a classification task that aims to assign a type of land to each pixel in satellite images. The dataset and application serve to prove that the final uncertainty bound enables the end-user to reason about the possible errors in the segmentation result. Second, Recurrent Neural Networks are used as a method to create robust models towards lacking datasets, both in terms of size and class balance. We apply them to two different fields, road extraction in satellite images and Wireless Capsule Endoscopy (WCE). The former demonstrates that contextual information in the temporal axis of data can be used to create models that achieve comparable results to state-of-the-art while being less complex. The latter, in turn, proves that contextual information for polyp detection can be crucial to obtain models that generalize better and obtain higher performance. Last, we propose two methods to leverage unlabeled data in the model creation process. Often datasets are easier to obtain than to label, which results in many wasted opportunities with traditional classification approaches. Our approaches based on self-supervised learning result in a novel contrastive loss that is capable of extracting meaningful information out of pseudo-labeled data. Applying both methods to WCE data proves that the extracted inherent knowledge creates models that perform better in extremely unbalanced datasets and with lack of data. To summarize, this thesis demonstrates potential solutions to obtain uncertainty bounds, provide reasonable explanations of the outputs, and to combat lack of data or unbalanced datasets. Overall, the presented methods have a positive impact on the DL field and could have a real and tangible effect for the society.[cat] És innegable que el Deep Learning ha causat una revolució en molts aspectes no solament de l’aprenentatge automàtic però també de les nostres vides diàries. Tot i així, encara queden aspectes a millorar. Les xarxes neuronals tenen problemes per estimar la seva confiança en les prediccions, i sovint reporten probabilitats altes en casos que no tenen relació amb el model o que directament no tenen sentit. De la mateixa forma, interpretar els resultats d’un model profund i complex resulta una tasca extremadament complicada. Aquests mateixos models, cada cop amb més paràmetres i més potents, requereixen també de dades més ben etiquetades i més completes. Tenint en compte aquestes limitacions, l’objectiu principal és el de buscar mètodes i algoritmes per trobar-ne solució. Primerament, es proposa la creació d’un mètode capaç d’obtenir incertesa en imatges satèl·lit i d’utilitzar-la per crear models més robustos i resultats interpretables. En segon lloc, s’utilitzen Recurrent Neural Networks (RNN) per combatre la falta de dades mitjançant l’obtenció d’informació contextual de dades temporals. Aquestes s’apliquen per l’extracció de carreteres d’imatges satèl·lit i per la classificació de pòlips en imatges obtingudes amb Wireless Capsule Endoscopy (WCE). Finalment, es plantegen dos mètodes per tractar amb la falta de dades etiquetades i desbalancejos en les classes amb l’ús de Self-supervised Learning (SSL). Seqüències no etiquetades d’imatges d’intestins s’incorporen en el models en una fase prèvia a la classificació tradicional. Aquesta tesi demostra que les solucions proposades per obtenir mesures d’incertesa són efectives per donar explicacions raonables i interpretables sobre els resultats. Igualment, es prova que el context en dades de caràcter temporal, obtingut amb RNNs, serveix per obtenir models més simples que poden arribar a solucionar els problemes derivats de la falta de dades. Per últim, es mostra que SSL serveix per combatre de forma efectiva els problemes de generalització degut a dades no balancejades en diversos dominis de WCE. Concloem que aquesta tesi presenta mètodes amb un impacte real en diversos aspectes de DL a la vegada que demostra la capacitat de tenir un impacte positiu en la societat

Deep Learning-based Solutions to Improve Diagnosis in Wireless Capsule Endoscopy

[eng] Deep Learning (DL) models have gained extensive attention due to their remarkable performance in a wide range of real-world applications, particularly in computer vision. This achievement, combined with the increase in available medical records, has made it possible to open up new opportunities for analyzing and interpreting healthcare data. This symbiotic relationship can enhance the diagnostic process by identifying abnormalities, patterns, and trends, resulting in more precise, personalized, and effective healthcare for patients. Wireless Capsule Endoscopy (WCE) is a non-invasive medical imaging technique used to visualize the entire Gastrointestinal (GI) tract. Up to this moment, physicians meticulously review the captured frames to identify pathologies and diagnose patients. This manual process is time- consuming and prone to errors due to the challenges of interpreting the complex nature of WCE procedures. Thus, it demands a high level of attention, expertise, and experience. To overcome these drawbacks, shorten the screening process, and improve the diagnosis, efficient and accurate DL methods are required. This thesis proposes DL solutions to the following problems encountered in the analysis of WCE studies: pathology detection, anatomical landmark identification, and Out-of-Distribution (OOD) sample handling. These solutions aim to achieve robust systems that minimize the duration of the video analysis and reduce the number of undetected lesions. Throughout their development, several DL drawbacks have appeared, including small and imbalanced datasets. These limitations have also been addressed, ensuring that they do not hinder the generalization of neural networks, leading to suboptimal performance and overfitting. To address the previous WCE problems and overcome the DL challenges, the proposed systems adopt various strategies that utilize the power advantage of Triplet Loss (TL) and Self-Supervised Learning (SSL) techniques. Mainly, TL has been used to improve the generalization of the models, while SSL methods have been employed to leverage the unlabeled data to obtain useful representations. The presented methods achieve State-of-the-art results in the aforementioned medical problems and contribute to the ongoing research to improve the diagnostic of WCE studies.[cat] Els models d’aprenentatge profund (AP) han acaparat molta atenció a causa del seu rendiment en una àmplia gamma d'aplicacions del món real, especialment en visió per ordinador. Aquest fet, combinat amb l'increment de registres mèdics disponibles, ha permès obrir noves oportunitats per analitzar i interpretar les dades sanitàries. Aquesta relació simbiòtica pot millorar el procés de diagnòstic identificant anomalies, patrons i tendències, amb la conseqüent obtenció de diagnòstics sanitaris més precisos, personalitzats i eficients per als pacients. La Capsula endoscòpica (WCE) és una tècnica d'imatge mèdica no invasiva utilitzada per visualitzar tot el tracte gastrointestinal (GI). Fins ara, els metges revisen minuciosament els fotogrames capturats per identificar patologies i diagnosticar pacients. Aquest procés manual requereix temps i és propens a errors. Per tant, exigeix un alt nivell d'atenció, experiència i especialització. Per superar aquests inconvenients, reduir la durada del procés de detecció i millorar el diagnòstic, es requereixen mètodes eficients i precisos d’AP. Aquesta tesi proposa solucions que utilitzen AP per als següents problemes trobats en l'anàlisi dels estudis de WCE: detecció de patologies, identificació de punts de referència anatòmics i gestió de mostres que pertanyen fora del domini. Aquestes solucions tenen com a objectiu aconseguir sistemes robustos que minimitzin la durada de l'anàlisi del vídeo i redueixin el nombre de lesions no detectades. Durant el seu desenvolupament, han sorgit diversos inconvenients relacionats amb l’AP, com ara conjunts de dades petits i desequilibrats. Aquestes limitacions també s'han abordat per assegurar que no obstaculitzin la generalització de les xarxes neuronals, evitant un rendiment subòptim. Per abordar els problemes anteriors de WCE i superar els reptes d’AP, els sistemes proposats adopten diverses estratègies que aprofiten l'avantatge de la Triplet Loss (TL) i les tècniques d’auto-aprenentatge. Principalment, s'ha utilitzat TL per millorar la generalització dels models, mentre que els mètodes d’autoaprenentatge s'han emprat per aprofitar les dades sense etiquetar i obtenir representacions útils. Els mètodes presentats aconsegueixen bons resultats en els problemes mèdics esmentats i contribueixen a la investigació en curs per millorar el diagnòstic dels estudis de WCE

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog