Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images

Wireless Capsule Endoscopy is a technique that allows for observation of the entire gastrointestinal tract in an easy and non-invasive way. However, its greatest limitation lies in the time required to analyze the large number of images generated in each examination for diagnosis, which is about 2 hours. This causes not only a high cost, but also a high probability of a wrong diagnosis due to the physician’s fatigue, while the variable appearance of abnormalities requires continuous concentration. In this work, we designed and developed a system capable of automatically detecting blood based on classification of extracted regions, following two different classification approaches. The first method consisted in extraction of hand-crafted features that were used to train machine learning algorithms, specifically Support Vector Machines and Random Forest, to create models for classifying images as healthy tissue or blood. The second method consisted in applying deep learning techniques, concretely convolutional neural networks, capable of extracting the relevant features of the image by themselves. The best results (95.7% sensitivity and 92.3% specificity) were obtained for a Random Forest model trained with features extracted from the histograms of the three HSV color space channels. For both methods we extracted square patches of several sizes using a sliding window, while for the first approach we also implemented the waterpixels technique in order to improve the classification resultsThis work was funded by the European Unions H2020: MSCA: ITN program for the “Wireless In-body Environment Communication WiBEC” project under the grant agreement no. 675353. Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.Pons Suñer, P.; Noorda, R.; Nevárez, A.; Colomer, A.; Pons Beltrán, V.; Naranjo, V. (2019). Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images. En Lecture Notes in Artificial Intelligence. Springer. 105-113. https://doi.org/10.1007/978-3-030-33617-2_12S105113Berens, J., Finlayson, G.D., Qiu, G.: Image indexing using compressed colour histograms. IEE Proc. Vis., Image Signal Process. 147(4), 349–355 (2000). https://doi.org/10.1049/ip-vis:20000630Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324Buscaglia, J.M., et al.: Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study. Clin. Gastroenterol. Hepatol. 6(3), 298–301 (2008). https://doi.org/10.1016/j.cgh.2007.12.029Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018Li, B., Meng, M.Q.H.: Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans. Biomed. Eng. 56(4), 1032–1039 (2009). https://doi.org/10.1109/TBME.2008.2010526Machairas, V., Faessel, M., Cárdenas-Peña, D., Chabardes, T., Walter, T., Decencière, E.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015). https://doi.org/10.1109/TIP.2015.2451011Novozámskỳ, A., Flusser, J., Tachecí, I., Sulík, L., Bureš, J., Krejcar, O.: Automatic blood detection in capsule endoscopy video. J. Biomed. Opt. 21(12), 126007 (2016). https://doi.org/10.1117/1.JBO.21.12.126007Signorelli, C., Villa, F., Rondonotti, E., Abbiati, C., Beccari, G., de Franchis, R.: Sensitivity and specificity of the suspected blood identification system in video capsule enteroscopy. Endoscopy 37(12), 1170–1173 (2005). https://doi.org/10.1055/s-2005-870410Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(1), 91 (2006). https://doi.org/10.1186/1471-2105-7-9

Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy

In capsule endoscopy (CE), preparation of the small bowel before the procedure is believed to increase visibility of the mucosa for analysis. However, there is no consensus on the best method of preparation, while comparison is difficult due to the absence of an objective automated evaluation method. The method presented here aims to fill this gap by automatically detecting regions in frames of CE videos where the mucosa is covered by bile, bubbles and remainders of food. We implemented two different machine learning techniques for supervised classification of patches: one based on hand-crafted feature extraction and Support Vector Machine classification and the other based on fine-tuning different convolutional neural network (CNN) architectures, concretely VGG-16 and VGG-19. Using a data set of approximately 40,000 image patches obtained from 35 different patients, our best model achieved an average detection accuracy of 95.15% on our test patches, which is similar to significantly more complex detection methods used for similar purposes. We then estimate the probabilities at a pixel level by interpolating the patch probabilities and extract statistics from these, both on per-frame and per-video basis, intended for comparison of different videos.This work was funded by the European Union’s H2020: MSCA: ITN program for the “Wireless In-body Environment Communication – WiBEC” project under the grant agreement no. 675353.Noorda, R.; Nevárez, A.; Colomer, A.; Naranjo, V.; Pons Beltrán, V. (2020). Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy. IEEE. 163-168. https://doi.org/10.1109/ISMICT.2019.8743878S16316

Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture

[EN] Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effective preparation method are conflicting due to the absence of objective, automatic cleanliness evaluation methods. In this work, we aim to provide such a method capable of presenting results on an intuitive scale, with a relatively light-weight novel convolutional neural network architecture at its core. We trained our model using 5-fold cross-validation on an extensive data set of over 50,000 image patches, collected from 35 different CE procedures, and compared it with state-of-the-art classification methods. From the patch classification results, we developed a method to automatically estimate pixel-level probabilities and deduce cleanliness evaluation scores through automatically learnt thresholds. We then validated our method in a clinical setting on 30 newly collected CE videos, comparing the resulting scores to those independently assigned by human specialists. We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. In the validation of our method, we found acceptable agreement with two human specialists compared to interhuman agreement, showing its validity as an objective evaluation method.This work was funded by the European Union's H2020: MSCA: ITN program for the "Wireless In-body Environment Communication - WiBEC" project under the grant agreement no. 675353. Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. Figures 2 and 3 were drawn by the authors.Noorda, R.; Nevárez, A.; Colomer, A.; Pons Beltrán, V.; Naranjo Ornedo, V. (2020). Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture. Scientific Reports. 10(1):1-13. https://doi.org/10.1038/s41598-020-74668-8S113101Pons Beltrán, V. et al. Evaluation of different bowel preparations for small bowel capsule endoscopy: a prospective, randomized, controlled study. Dig. Dis. Sci. 56, 2900–2905. https://doi.org/10.1007/s10620-011-1693-z (2011).Klein, A., Gizbar, M., Bourke, M. J. & Ahlenstiel, G. Validated computed cleansing score for video capsule endoscopy. Dig. Endosc. 28, 564–569. https://doi.org/10.1111/den.12599 (2016).Vilarino, F., Spyridonos, P., Pujol, O., Vitria, J. & Radeva, P. Automatic detection of intestinal juices in wireless capsule video endoscopy. In 18th International Conference on Pattern Recognition (ICPR’06), Vol. 4, 719–722, https://doi.org/10.1109/ICPR.2006.296 (2006).Wang, Q. et al. Reduction of bubble-like frames using a rss filter in wireless capsule endoscopy video. Opt. Laser Technol. 110, 152–157. https://doi.org/10.1016/j.optlastec.2018.08.051 (2019).Mewes, P. W. et al. Automatic region-of-interest segmentation and pathology detection in magnetically guided capsule endoscopy. In International Conference on Medical Image Computing and Computer-Assisted Intervention 141–148, https://doi.org/10.1007/978-3-642-23626-6_18 (Springer 2011).Bashar, M. K., Mori, K., Suenaga, Y., Kitasaka, T. & Mekada, Y. Detecting informative frames from wireless capsule endoscopic video using color and texture features. In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), 603–610, https://doi.org/10.1007/978-3-540-85990-1_72 (Springer, Berlin, 2008).Sun, Z., Li, B., Zhou, R., Zheng, H. & Meng, M. Q. H. Removal of non-informative frames for wireless capsule endoscopy video segmentation. In 2012 IEEE International Conference on Automation and Logistics, 294–299, https://doi.org/10.1109/ICAL.2012.6308214 (2012).Khun, P. C., Zhuo, Z., Yang, L. Z., Liyuan, L. & Jiang, L. Feature selection and classification for wireless capsule endoscopic frames. In 2009 International Conference on Biomedical and Pharmaceutical Engineering, 1–6, https://doi.org/10.1109/ICBPE.2009.5384106 (2009).Segui, S. et al. Categorization and segmentation of intestinal content frames for wireless capsule endoscopy. IEEE Trans. Inf Technol. Biomed. 16, 1341–1352. https://doi.org/10.1109/TITB.2012.2221472 (2012).Maghsoudi, O. H., Talebpour, A., Soltanian-Zadeh, H., Alizadeh, M. & Soleimani, H. A. Informative and uninformative regions detection in wce frames. J. Adv. Comput. 3, 12–34. https://doi.org/10.7726/jac.2014.1002a (2014).Noorda, R., Nevarez, A., Colomer, A., Naranjo, V. & Pons, V. Automatic detection of intestinal content to evaluate visibility in capsule endoscopy. In $$13^{th}$$International Symposium on Medical Information and Communication Technology (ISMICT 2019) (Oslo, Norway, 2019).Andrearczyk, V. & Whelan, P. F. Deep learning in texture analysis and its application to tissue image classification. In Biomedical Texture Analysis (eds Depeursinge, A. et al.) 95–129 (Elsevier, Amsterdam, 2017). https://doi.org/10.1016/B978-0-12-812133-7.00004-1.Werbos, P. J. et al. Backpropagation through time: what it does and how to do it. Proc. IEEE 78, 1550–1560. https://doi.org/10.1109/5.58337 (1990).Jia, X. & Meng, M. Q.-H. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 639–642, https://doi.org/10.1109/EMBC.2016.7590783 (IEEE, 2016).Simonyan, K. & Zisserman, A. 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Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

[EN] Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the ¿Right¿ or ¿Leak¿ states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different su-pervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, repre-senting an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration.The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development Fund.Zazo-Manzaneque, R.; Pons-Beltrán, V.; Vidaurre, A.; Santonja, A.; Sánchez-Diaz, C. (2022). Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System. Sensors. 22(14):1-18. https://doi.org/10.3390/s22145211118221

Dielectric characterization of healthy and malignant colon tissues in the 0.5 18 GHz frequency band

Several reports over the last few decades have shown that the dielectric properties of healthy and malignant tissues of the same body organ usually show different values. However, no intensive dielectric studies of human colon tissue have been performed, despite colon cancer's being one of the most common types of cancer in the world. In order to provide information regarding this matter, a dielectric characterization of healthy and malignant colon tissues is presented. Measurements are performed on ex vivo surgery samples obtained from 20 patients, using an open-ended coaxial probe in the 0.5 18 GHz frequency band. Results show that the dielectric constant of colon cancerous tissue is 8.8% higher than that of healthy tissues (p = 0.002). Besides, conductivity is about 10.6% higher, but in this case measurements do not have statistical significance (p = 0.038). Performing an analysis per patient, the differences in dielectric constant between healthy and malignant tissues appear systematically. Particularized results for specific frequencies (500 MHz, 900 MHz, 2.45 GHz, 5 GHz, 8.5 GHz and 15 GHz) are also reported. The findings have potential application in early-stage cancer detection and diagnosis, and can be useful in developing new tools for hyperthermia treatments as well as creating electromagnetic models of healthy and cancerous tissues.The authors would like to thank the medical staff of the endoscopy unit of Hospital Universitari i Politecnic La Fe for their assistance in the gathering of tissue samples. This work was supported by Ministerio de Economia y Competitividad, Spain (ref. TEC2014-60258-C2-1-R, TEC2014-56469-REDT), by FEDER funds, and by a UPV-IISLaFe action (CEI-2G, 2014).Fornés Leal, A.; García Pardo, C.; Frasson, M.; Pons Beltrán, V.; Cardona Marcet, N. (2016). Dielectric characterization of healthy and malignant colon tissues in the 0.5 18 GHz frequency band. Physics in Medicine and Biology. 61(20):7334-7346. https://doi.org/10.1088/0031-9155/61/20/7334S73347346612

Diseño y desarrollo de un sistema para la detección automática de sangre en imágenes de cápsula endoscópica

La endoscopia por cápsula inalámbrica permite observar el tracto gastrointestinal completo de forma sencilla y no invasiva. Sin embargo, se genera una gran cantidad de imágenes por examen que los médicos tardan aproximadamente 2 horas en analizar. Esto no solo supone un elevado coste, sino que el diagnóstico puede ser erróneo debido a la fatiga y a la naturaleza variable de las lesiones, que exige una alta concentración. En el presente trabajo se diseña y desarrolla un sistema capaz de detectar automáticamente aquellas imágenes que contienen sangre, siguiendo dos enfoques distintos. El primero consiste en escoger y extraer ciertas características de color de las imágenes con las que entrenar modelos de aprendizaje automático clásico (SVM y Random Forest) que permitan distinguir entre tejido sano y sangre. Además, se implementa la técnica de segmentación “waterpixels” para tratar de mejorarla clasificación. El segundo método consiste en utilizar técnicas de aprendizaje profundo (redes neuronales convolucionales), capaces de extraer las características relevantes de la imagen por sí solas. La configuración que ha obtenido los mejores resultados (95,7% de sensibilidad y 92,3% de especificidad) ha sido un modelo Random Forest entrenado con los histogramas de los canales del espacio de color HSVPons Suñer, P.; Noorda, R.; Naranjo, V.; Nevárez Heredia, A.; Pons Beltrán, V. (2018). Diseño y desarrollo de un sistema para la detección automática de sangre en imágenes de cápsula endoscópica. VISILAB. 257-260. http://hdl.handle.net/10251/136066S25726

Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization

[EN] In recent years, localization for capsule endoscopy applications using Ultra-Wideband (UWB) technology has become an attractive field of study due to its potential benefits for patients. Performance analysis of RF-based localization techniques are very limited in literature. Most of the available studies rely on software simulations using digital human models. Nonetheless, no realistic studies based on in-vivo measurements has been reported yet. This paper investigates the performance of RSS-based technique for three-dimensional (3D) localization in the UWB frequency band. Impact of receivers selection as well as of the evaluated path loss model on the localization accuracy is investigated. Results obtained through CST-based simulations and from recently conducted in-vivo measurements are presented and compared.This work was supported by the European Union's H2020:MSCA:ITN program for the "Wireless In-body Environment Communication- WiBEC" project under the grant agreement no. 675353. This work was also funded by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Barbi, M.; Garcia-Pardo, C.; Cardona Marcet, N.; Andrea Nevárez; Vicente Pons Beltrán; Frasson, M. (2018). Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization. IEEE. 340-344. https://doi.org/10.1109/PIMRC.2018.8580862S34034

Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models

[EN] Access to the small bowel by means of an enteroscope is difficult, even using current devices such as single-balloon or double-balloon enteroscopes. Exploration time and patient discomfort are the main drawbacks. The prototype 'Endoworm' analysed in this paper is based on a pneumatic translation system that, gripping the bowel, enables the endoscope to move forward while the bowel slides back over its most proximal part. The grip capacity is related to the pressure inside the balloon, which depends on the insufflate volume of air. Different materials were used as in vitro and ex vivo models: rigid polymethyl methacrylate, flexible silicone, polyester urethane and ex vivo pig small bowel. On measuring the pressure-volume relationship, we found that it depended on the elastic properties of the lumen and that the frictional force depended on the air pressure inside the balloons and the lumen's elastic properties. In the presence of a lubricant, the grip on the simulated intestinal lumens was drastically reduced, as was the influence of the lumen's properties. This paper focuses on the Endoworm's ability to grip the bowel, which is crucial to achieving effective endoscope forward advance and bowel foldingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development FundTobella, J.; Pons-Beltrán, V.; Santonja, A.; Sánchez-Diaz, C.; Campillo Fernandez, AJ.; Vidaurre, A. (2020). Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 234(5):1-10. https://doi.org/10.1177/09544119209014141102345Iddan, G., Meron, G., Glukhovsky, A., & Swain, P. (2000). Wireless capsule endoscopy. Nature, 405(6785), 417-417. doi:10.1038/35013140Yamamoto, H., Sekine, Y., Sato, Y., Higashizawa, T., Miyata, T., Iino, S., … Sugano, K. (2001). Total enteroscopy with a nonsurgical steerable double-balloon method. Gastrointestinal Endoscopy, 53(2), 216-220. doi:10.1067/mge.2001.112181Arnott, I. D. R., & Lo, S. K. (2004). REVIEW: The Clinical Utility of Wireless Capsule Endoscopy. Digestive Diseases and Sciences, 49(6), 893-901. doi:10.1023/b:ddas.0000034545.58486.e6Hosoe, N., Takabayashi, K., Ogata, H., & Kanai, T. (2019). Capsule endoscopy for small‐intestinal disorders: Current status. 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Endoworm: A new semi-autonomous enteroscopy device

[EN] Using enteroscopes with therapeutic capacity to explore the small intestine entails certain limitations, including long exploration times, patient discomfort, the need for sedation, a high percentage of incomplete explorations and a long learning curve. This article describes the advances and setbacks encountered in designing the new Endoworm enteroscopy system, a semi-autonomous device consisting of a control unit and three cavities that inflate and deflate in such a way that the bowel retracts over the endoscope. The system can be adapted to any commercial enteroscope. Endoworm was tested in different intestine models: a polymethyl methacrylate rigid tube, an in vitro polyester urethane model, an ex vivo pig model and an in vivo animal model. The general behavior of the prototype was evaluated by experienced medical personnel. The mean distance covered through the lumen was measured in each cycle. The system was found to have excellent performance in the rigid tube and in the in vitro model. The ex vivo tests showed that the behavior depended largely on the mechanical properties of the lumen, while the in vivo experiments suggest that the device will require further modifications to improve its performance.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through Project PI12/01000 and also from UPV/IIS LA Fe through the Endoworm 3.0 Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program and CIBER Actions, and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development Fund.Sánchez-Diaz, C.; Senent-Cardona, E.; Pons, V.; Santonja Gimeno, AV.; Vidaurre Garayo, AJ. (2018). Endoworm: A new semi-autonomous enteroscopy device. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 232(11):1137-1143. https://doi.org/10.1177/0954411918806330S113711432321

Dielectric Characterization of In Vivo Abdominal and Thoracic Tissues in the 0.5 26.5 GHz Frequency Band for Wireless Body Area Networks

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The dielectric properties of biological tissues are of utmost importance in the development of wireless body area networks (WBANs), especially for implanted devices. The early design stages of medical devices like capsule endoscopy, pacemakers, or physiological sensors rely on precise knowledge of the dielectric properties of the tissues present in their surrounding medium. Many of these applications make use of electromagnetic phantoms, which are software or physical models that imitate the shape and the electromagnetic properties of the tissues. They are used for designing devices in software simulations and for testing them in laboratory trials, aiding in both the development of WBAN antennas or in communication link evaluations. The existing reports about dielectric in vivo properties are limited and have drawbacks like: low variety of characterized tissues, lacking some relevant ones, and limitations and inhomogeneity in the measured frequency range. This paper aims at filling that gap by providing a new database of dielectric properties of biological tissues measured in vivo . In particular, it is focused on the tissues of the thoracic and the abdominal regions, measured at the same wide frequency band, on the same animal specimen, and under the same conditions. The properties have been obtained by measuring porcine tissues in the 0.5¿26.5 GHz band with the open-ended coaxial technique. In this paper, we focus on those tissues that have been scarcely characterized so far in the literature, like heart, esophagus, stomach, and pancreas. The Cole¿Cole fitting parameters of the measured tissues and their uncertainties are provided.This work was supported in part by UPV-IIS LaFe Program (STuDER, 2016, and EMOTE, 2018), in part by the Programa de Ayudas de Investigacion y Desarrollo (PAID-01-16) from the Universitat Politecnica de Valencia, in part by the European Union's H2020: MSCA: ITN Programs for the "Wireless In-Body Environment Communication-WiBEC'' Project, under Grant 675353, and in part by the "mmWave Communications in the Built Environments-WaveComBE'' Project, under Grant 766231.Fornés Leal, A.; Cardona Marcet, N.; Frasson, M.; Castelló-Palacios, S.; Nevárez, A.; Pons Beltrán, V.; Garcia-Pardo, C. (2019). Dielectric Characterization of In Vivo Abdominal and Thoracic Tissues in the 0.5 26.5 GHz Frequency Band for Wireless Body Area Networks. IEEE Access. 7:31854-31864. https://doi.org/10.1109/ACCESS.2019.2903481S3185431864