Wireless capsule gastrointestinal endoscopy: direction of arrival estimation based localization survey

One of the significant challenges in Capsule Endoscopy (CE) is to precisely determine the pathologies location. The localization process is primarily estimated using the received signal strength from sensors in the capsule system through its movement in the gastrointestinal (GI) tract. Consequently, the wireless capsule endoscope (WCE) system requires improvement to handle the lack of the capsule instantaneous localization information and to solve the relatively low transmission data rate challenges. Furthermore, the association between the capsule’s transmitter position, capsule location, signal reduction and the capsule direction should be assessed. These measurements deliver significant information for the instantaneous capsule localization systems based on TOA (time of arrival) approach, PDOA (phase difference of arrival), RSS (received signal strength), electromagnetic, DOA (direction of arrival) and video tracking approaches are developed to locate the WCE precisely. The current article introduces the acquisition concept of the GI medical images using the endoscopy with a comprehensive description of the endoscopy system components. Capsule localization and tracking are considered to be the most important features of the WCE system, thus the current article emphasizes the most common localization systems generally, highlighting the DOA-based localization systems and discusses the required significant research challenges to be addressed

Determining the Position and Orientation of In-body Medical Instruments Using Reactive Magnetic Field Mapping

There has been a huge demand for localizing in-body medical instruments (IBMI), such as wireless capsule endoscope (WCE) and nasogastric tube (NGT). Some stud ies have been conducted to solve this issue over the last three decades. In these studies, they either used a permanent magnet (PM), a static current source (SCS), radio frequency (RF) fields or integration of two of these. The PM is a stable and reliable magnetic field source. However, due to the size restriction of the NGT and the WCE, only a small PM can be used. Subsequently, the small size issue causes low power delivery at the larger tracking distance. Also, the PM field is very susceptible to ambient noise, and the PM-based localization is not possible in ap plications requiring robotic actuation. Even though an SCS can be used to replace the permanent magnet, and thus the current level can be varied in relation to the dis tance for optimized power delivery, it requires a relatively high power to generate a higher strength magnetic field. Consequently, a more powerful and larger battery is needed to feed the circuit.Radio frequency field sources require high frequencies to achieve sufficient precision, but these frequencies undergo high attenuation in the body. Therefore, the low-frequency RF field is preferred 1 . In the near-field 2 , plane wave assumption of the far-field fails for localization methods since the waves in this region are spherical. Hence, the wave-front has to be formulated by both the range and the direction of arrival (DOA). The DOA requires the phase difference of neighbouring sensors to be calculated. However, if the operating wavelength is much larger than the distance between the source and the receiver, it is not feasible to compute the phase difference between the neigh bouring sensors. Thus, there are numerous algorithms in the literature to overcome these issues, such as MUSIC or ESPRIT which are either complicated or computa tionally expensive. In RF-based localization, generally the time of arrival (TA), the time differ ence of arrival (TDA), the angle of arrival (AOA) and the received signal strength (RSS) are widely used for localization. However, the TA and TDA require accu rate knowledge of field speed and good time synchronization. It is not possible to accurately know while travelling through the body tissues due to complexity of the tissues. The AOA is also impractical for intra-body applications owing to multiple reflections signal from the tissues, commonly known as the multipath effect. The RSS precision is dependent on good knowledge of power loss in complex body tis sues. Also, the RSS method requires accurate knowledge of the transmitted signal strength. However, the power of transmitted frequencies may vary due to the ca pacitive effect of human tissue on Resonant frequency of source, hence RSS-based techniques prove difficult in practice. Therefore, a novel method of mapping the magnetic field vector in the near field region is proposed. This magnetic field mapping (MFM) uses single-axis coils placed orthogonally with respect to a sensor plane (SP). These single-axis sensors pick up only the orthogonal component of the magnetic field, which varies as a function of the orientation of the source and distance to the source. Thus, using this information, the field strength captured by each sensor is mapped to its correspond ing position on the SP as pixels. Next, these field strengths with known positions are used to detect the location and orientation of the field source relative to the SP. MATLAB and CST Microwave simulation were conducted, and many laboratory experiments were performed, and we show that the novel technique not only over comes the issues faced in the methods mentioned above but also accomplishes an accurate source positioning with a precision of better than ± 0.5 cm in 3-D and orientation with a maximum error of ±5◦

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

Design of an Electromagnetic Coil Array for Wireless Endoscope Capsule Localization

Wireless capsule endoscopy is a technique that visualizes mucosa of gastrointestinal tract. The first wireless capsule endoscope was developed in 2001, and since then, the tech-nology has been in constant development. With its reduced amount of discomfort, ability to visualize the whole gastrointestinal tract and many interesting future prospects, capsule en-doscopy is challenging the conventional procedure, in which a camera module at the end of a tube is inserted to the gastrointestinal tract. In general, the method can be used to diagnose many diseases, such as cancers, Celiac disease and Crohn’s disease. In order to achieve all the future prospects of the technology, for example, active steering of the capsule, biopsy and drug-delivery with the capsule, challenge of detecting the cap-sule’s position and orientation inside human needs to be overcome. Therefore, the localiza-tion challenge is considered in this thesis. The aim is to select an optimal method for localiza-tion based on current research, and to model and develop a prototype that could be utilized in capsule localization. The designed sensor array is evaluated with the help of finite element method -based modelling, sensitivity analysis and practical experiments. Based on the studied literature, an active magnetic field strength -based localization tech-nique was selected for further analysis. According to the literature, the method provides high accuracy and could also be utilized in other purposes, for example, wireless charging and ac-tive locomotion of the capsule. In addition, the method does not suffer from attenuation of the fields within a human body. Therefore, theoretical basis of the magnetic field strength -based localization was presented, and four electromagnetic coil arrays were designed for sensitivity analysis. Contrary to many developed arrays seen in the literature review, all the designed systems were planar, in order to develop a system that could be fitted, for instance, inside a hospital bed. Based on the sensitivity analysis, an array with relatively large sensitivity and optimal number of measurement channels was selected for practical study. In addition, pos-sible markers that could be fitted inside a regular sized endoscopic capsule were numerically modelled. It was found that a resonated solenoid marker with ferrite core causes the largest voltage compared to other modelled targets, and therefore it was constructed for experi-ments. The whole array was built and equipped with electronics and measurement devices to be able to perform testing. Two channels of the array were selected for example measurements, and the results were analysed and compared with modelled values and sensitivity patterns. The system was tested at multiple different heights and positions, and the effect of changing the marker’s orientation with respect to the array was analysed. It was found that the system gives a reasonable response when the marker is oriented along the excitation magnetic field. With this orientation, it was possible to measure significant voltages caused by the marker even at distance of 25 cm from the array. However, when the marker was oriented so that the excitation field could not properly excite it, the measured voltages got smaller. In addition, at certain orientation, the measured voltages did not seem reliable because voltage caused by the marker could not been discriminated from the noise of the system. The study indicates that the method is suitable for localization of resonated solenoid sample with ferrite core, but further improvements are needed to make the system work at each position and orientation of the marker. In addition, an inversion algorithm that estimates marker’s position and orien-tation based on the measured voltage needs to be integrated to the system

Detecting Diseases in Gastrointestinal Biopsy Images Using CNN

Discovering illnesses in gastrointestinal biopsy photos is a complicated job that must be executed swiftly and with an excellent level of precision. In the interest of boosting the precision and rapidity of illness identification in clinical photographs, models based on deep learning have shown flashes of brilliance. This paper outlines the processes for designing a deep learning model to identify diseases in gastrointestinal biopsy imagery. The procedures involve gathering data, processing, adopting a model, training, assessing and optimizing. It is observed that in order to certify the detection rate, trustworthiness, and safety for clinical evaluation, it is vital that it be developed together with experts in deep learning and medical imaging.&nbsp

Cooperative Magneto-Inductive Localization

Wireless localization is a key requirement for many applications. It concerns position estimation of mobile nodes (agents) relative to fixed nodes (anchors) from wireless channel measurements. Cooperative localization is an advanced concept that considers the joint estimation of multiple agent positions based on channel measurements of all agent-anchor links together with all agent-agent links. In this paper we present the first study of cooperative localization for magneto-inductive wireless sensor networks, which are of technological interest due to good material penetration and channel predictability. We demonstrate significant accuracy improvements (a factor of 3 for 10 cooperating agents) over the non-cooperative scheme. The evaluation uses the Cram\'er-Rao lower bound on the cooperative position estimation error, which is derived herein. To realize this accuracy, the maximum-likelihood estimate (MLE) must be computed by solving a high-dimensional least-squares problem, whereby convergence to local minima proves to be problematic. A proposed cooperative localization algorithm addresses this issue: first, preliminary estimates of the agent positions and orientations are computed, which then serve as initial values for a gradient search. In all our test cases, this method yields the MLE and the associated high accuracy (comprising the cooperation gain) from a single solver run. The preliminary estimates use novel closed-form MLE formulas of the distance, direction and orientation for single links between three-axis coils, which are given in detail.Comment: To appear at the IEEE PIMRC 2021 conference. This work has been submitted to the IEEE for publication. Copyright may be transferred without notic

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

Localization and Tracking of Intestinal Paths for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) is a non-invasive technology used for visual inspection of the human gastrointestinal (GI) tract. Localization of the capsule is a vital component of the system, as this enables physicians to identify the position of abnormalities. Several approaches exist that use the received signal strength (RSS) of the radio frequency (RF) signals for localization. However, few of these utilize the sparseness of the signals. Due to intestinal motility, the capsule positions will change with time. The distance travelled by the capsule in the intestine, however, remains more or less constant with time. In this thesis, a compressive sensing (CS) based localization algorithm is presented, that utilize signal sparsity in the RSS measurements. Different L1-minimization algorithms are used to find the sparse location vector. The performance is evaluated by electromagnetic (EM) simulations performed on a human voxel model, using narrow-band (NB) and ultra wide-band (UWB) signals. From intestinal positions, the distance the capsule has travelled is estimated by use of Kalman- and particle filters. It was found that localization accuracy of a few millimeters is possible under ideal conditions, when the RSS measurements are generated from a path loss model. When using path loss data from the EM simulations, localization accuracy on the order of 20-30 mm was achievable for NB signals. Use of UWB signals resulted in localization errors between 35-60 mm, depending on frequency range and bandwidth. From generated intestinal positions, the travelled distance was estimated with a minimum accuracy of a few millimeters, when using a VNL Kalman filter and moderate amounts of observation noise. The results are found from a limited amount of data. In order to increase the confidence in the presented results, the performance of the localization algorithm and the filters should be evaluated with a larger number of datasets