Generic Feature Learning for Wireless Capsule Endoscopy Analysis

The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)

Visual Odometer on Videos of Endoscopic Capsules (VOVEC)

Desde a sua introdução em 2001, as cápsulas endoscópicas tornaram-se o principal método para obter imagens do intestino - uma região de difícil acesso através de métodos de endoscopia tradicionais - revolucionando a maneira como os diagnósticos no campo das doenças do intestino delgado são feitos. Estas cápsulas com dimensões comparáveis a um comprimido vitamínico tiram partido de uma câmera wireless para criar vídeos de 8 a 10 horas do trato digestivo dos pacientes. Devido à longa duração dos vídeos produzidos, o diagnóstico humano é moroso, entediante e propício a erros. Para além disto, depois de encontrada uma lesão, a informação da sua localização é escassa e dependente de hardware externo, levando a que uma solução baseada apensa em software com precisão melhorada seja bastante desejada. Este trabalho advém desta necessidade e, tendo-a em mente, propomos a implementação de dois métodos baseados em deep-learning, visando melhorar em relação às limitações dos sistemas atuais de localização de cápsulas endoscópicas. Para treinar e testar as nossas redes neuronais, um dataset que contém 111 vídeos da cápsula PillCam SB3 e 338 da cápsula PillCam SB2 foi utilizado, cortesia do Centro Hospitalar do Porto (CHP).O primeiro método consiste numa simples estimação do deslocamento da cápsula ao longo do intestino delgado utilizando uma HomographyNet, uma abordagem de deep-learning supervisionado usada para o cálculo de homografias entre imagens.Já no segundo método uma posição relativa 3D da cápsula é fornecida ao longo do intestino delgado, recorrendo a um método não-supervisionado de deep-learning denominado SfMLearner. Este método combina uma DepthNet e uma PoseNet para aprender a profundidade da imagem e a posição da cápsula em simultâneo.Since its introduction in 2001, capsule endoscopy has become the leading screening method for the small bowel - a region not easily accessible with traditional endoscopy techniques - revolutionizing the way diagnostics work in the field of small bowel diseases. These capsules are vitamin-sized and leverage from a small wireless camera to create 8 to 10 hour videos of the patients digestive tract. Due to the long duration of the videos produced, the human-based diagnosis is elongated, tedious and error-prone. Moreover, once a lesion is found, the localization information is scarce and hardware dependent, entailing desirability for a software-only endoscopic capsule localization system with added precision. This work stems from this need and, bearing this in mind, we propose the implementation of two deep-learning based methods to improve upon the limitations of the techniques used so far for the capsule position estimation. To train and test our networks, a dataset of 111 PillCam SB3 and 338 PillCam SB2 videos were used, courtesy of Centro Hospitalar do Porto (CHP).The first method consists in a simple capsule displacement estimation throughout the small bowel utilizing HomographyNet, a deep learning supervised approach that is used for homography computation between images. (DeTone et al. (2016))Differently, the second proposed method is intended to provide a 3D position along the small intestine, utilizing a deep learning unsupervised approach labeled SfMLearner, which takes advantage of a combination between a DepthNet and a PoseNet to learn depth and ego-motion from video simultaneously. (Zhou et al. (2017)

Real-time currency recognition on video using AKAZE algorithm

Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data

Real-time endoscopic mosaicking

Abstract. With the advancement of minimally invasive techniques for surgical and diagnostic procedures, there is a growing need for the development of methods for improved visualization of internal body structures. Video mosaicking is one method for doing this. This approach provides a broader field of view of the scene by stitching together images in a video sequence. Of particular importance is the need for online processing to provide real-time feedback and visualization for image-guided surgery and diagnosis. We propose a method for online video mosaicking applied to endoscopic imagery, with examples in microscopic retinal imaging and catadioptric endometrial imaging

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

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community