Tongue Tumor Detection in Medical Hyperspectral Images

A hyperspectral imaging system to measure and analyze the reflectance spectra of the human tongue with high spatial resolution is proposed for tongue tumor detection. To achieve fast and accurate performance for detecting tongue tumors, reflectance data were collected using spectral acousto-optic tunable filters and a spectral adapter, and sparse representation was used for the data analysis algorithm. Based on the tumor image database, a recognition rate of 96.5% was achieved. The experimental results show that hyperspectral imaging for tongue tumor diagnosis, together with the spectroscopic classification method provide a new approach for the noninvasive computer-aided diagnosis of tongue tumors

Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment

International audienc

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring

Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physician’s perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by “imitating” the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the technique’s full potential. The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible. To overcome these hurdles and enable functional laparoscopy, for the first time in this field physical models are combined with powerful machine learning techniques. The physical model is employed to create highly accurate simulations, which in turn teach the algorithm to rapidly relate multispectral pixels to underlying functional changes. To reduce the domain shift introduced by learning from simulations, a novel transfer learning approach automatically adapts generic simulations to match almost arbitrary recordings of visceral tissue. In combination with the only available video-rate capable multispectral sensor, the method pioneers fluent perfusion monitoring with MSI. This system was carefully tested in a multistage process, involving in silico quantitative evaluations, tissue phantoms and a porcine study. Clinical applicability was ensured through in-patient recordings in the context of partial nephrectomy; in these, the novel system characterized ischemia live during the intervention. Verified against a fluorescence reference, the results indicate that fluent, non-invasive ischemia detection and monitoring is now possible. In conclusion, this thesis presents the first multispectral laparoscope capable of videorate functional analysis. The system was successfully evaluated in in-patient trials, and future work should be directed towards evaluation of the system in a larger study. Due to the broad applicability and the large potential clinical benefit of the presented functional estimation approach, I am confident the descendants of this system are an integral part of the next generation OR

Retainer-Free Optopalatographic Device Design and Evaluation as a Feedback Tool in Post-Stroke Speech and Swallowing Therapy

Stroke is one of the leading causes of long-term motor disability, including oro-facial impairments which affect speech and swallowing. Over the last decades, rehabilitation programs have evolved from utilizing mainly compensatory measures to focusing on recovering lost function. In the continuing effort to improve recovery, the concept of biofeedback has increasingly been leveraged to enhance self-efficacy, motivation and engagement during training. Although both speech and swallowing disturbances resulting from oro-facial impairments are frequent sequelae of stroke, efforts to develop sensing technologies that provide comprehensive and quantitative feedback on articulator kinematics and kinetics, especially those of the tongue, and specifically during post-stroke speech and swallowing therapy have been sparse. To that end, such a sensing device needs to accurately capture intraoral tongue motion and contact with the hard palate, which can then be translated into an appropriate form of feedback, without affecting tongue motion itself and while still being light-weight and portable. This dissertation proposes the use of an intraoral sensing principle known as optopalatography to provide such feedback while also exploring the design of optopalatographic devices itself for use in dysphagia and dysarthria therapy. Additionally, it presents an alternative means of holding the device in place inside the oral cavity with a newly developed palatal adhesive instead of relying on dental retainers, which previously limited device usage to a single person. The evaluation was performed on the task of automatically classifying different functional tongue exercises from one another with application in dysphagia therapy, whereas a phoneme recognition task was conducted with application in dysarthria therapy. Results on the palatal adhesive suggest that it is indeed a valid alternative to dental retainers when device residence time inside the oral cavity is limited to several tens of minutes per session, which is the case for dysphagia and dysarthria therapy. Functional tongue exercises were classified with approximately 61 % accuracy across subjects, whereas for the phoneme recognition task, tense vowels had the highest recognition rate, followed by lax vowels and consonants. In summary, retainer-free optopalatography has the potential to become a viable method for providing real-time feedback on tongue movements inside the oral cavity, but still requires further improvements as outlined in the remarks on future development.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7.2.1 Optode placement and circuit board layout . . . . . . . . . . . . . . . . 87 7.2.2 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.3 Device characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3.1 Photodiode transient response . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.2 Current source and rise time . . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.3 Multiplexer switching speed . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.4 Measurement cycle and firmware implementation . . . . . . . . . . . . . 93 7.3.5 In vitro measurement accuracy . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.6 Optode measurement stability . . . . . . . . . . . . . . . . . . . . . . . 96 7.4 Evaluation on the phoneme recognition task . . . . . . . . . . . . . . . . . . . . 98 7.4.1 Corpus selection and recording setup . . . . . . . . . . . . . . . . . . . . 98 7.4.2 Annotation and sensor data post-processing . . . . . . . . . . . . . . . . 98 7.4.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 99 7.4.4 Classifier and feature selection . . . . . . . . . . . . . . . . . . . . . . . 100 7.4.5 Evaluation paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.5.1 Tongue distance curve prediction . . . . . . . . . . . . . . . . . . . . . . 105 7.5.2 Tongue contact patterns and contours . . . . . . . . . . . . . . . . . . . 105 7.5.3 Phoneme recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 7.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8 Conclusion and future work 115 9 Appendix 9.1 Analytical light transport models . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9.2 Meshed Monte Carlo method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 9.3 Laser safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.4 Current source modulation voltage . . . . . . . . . . . . . . . . . . . . . . . . . 123 9.5 Transimpedance amplifier’s frequency responses . . . . . . . . . . . . . . . . . . 123 9.6 Initial OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . . . 127 9.7 Improved OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . 129 9.8 Test station layout drawing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Bibliography 152Der Schlaganfall ist eine der häufigsten Ursachen für motorische Langzeitbehinderungen, einschließlich solcher im Mund- und Gesichtsbereich, deren Folgen u.a. Sprech- und Schluckprobleme beinhalten, welche sich in den beiden Symptomen Dysarthrie und Dysphagie äußern. In den letzten Jahrzehnten haben sich Rehabilitationsprogramme für die Behandlung von motorisch ausgeprägten Schlaganfallsymptomatiken substantiell weiterentwickelt. So liegt nicht mehr die reine Kompensation von verlorengegangener motorischer Funktionalität im Vordergrund, sondern deren aktive Wiederherstellung. Dabei hat u.a. die Verwendung von sogenanntem Biofeedback vermehrt Einzug in die Therapie erhalten, um Motivation, Engagement und Selbstwahrnehmung von ansonsten unbewussten Bewegungsabläufen seitens der Patienten zu fördern. Obwohl jedoch Sprech- und Schluckstörungen eine der häufigsten Folgen eines Schlaganfalls darstellen, wird diese Tatsache nicht von der aktuellen Entwicklung neuer Geräte und Messmethoden für quantitatives und umfassendes Biofeedback reflektiert, insbesondere nicht für die explizite Erfassung intraoraler Zungenkinematik und -kinetik und für den Anwendungsfall in der Schlaganfalltherapie. Ein möglicher Grund dafür liegt in den sehr strikten Anforderungen an ein solche Messmethode: Sie muss neben Portabilität idealerweise sowohl den Kontakt zwischen der Zunge und dem Gaumen, als auch die dreidimensionale Bewegung der Zunge in der Mundhöhle erfassen, ohne dabei die Artikulation selbst zu beeinflussen. Um diesen Anforderungen gerecht zu werden, wird in dieser Dissertation das Messprinzip der Optopalatographie untersucht, mit dem Schwerpunkt auf der Anwendung in der Dysarthrie- und Dysphagietherapie. Dies beinhaltet auch die Entwicklung eines entsprechenden Gerätes sowie dessen Befestigungsmethode in der Mundhöhle über ein dediziertes Mundschleimhautadhäsiv. Letzteres umgeht das bisherige Problem der notwendigen Anpassung eines solchen intraoralen Gerätes an einen einzelnen Nutzer. Für die Anwendung in der Dysphagietherapie erfolgte die Evaluation anhand einer automatischen Erkennung von Mobilisationsübungen der Zunge, welche routinemäßig in der funktionalen Dysphagietherapie durchgeführt werden. Für die Anwendung in der Dysarthrietherapie wurde eine Lauterkennung durchgeführt. Die Resultate bezüglich der Verwendung des Mundschleimhautadhäsives suggerieren, dass dieses tatsächlich eine valide Alternative zu den bisher verwendeten Techniken zur Befestigung intraoraler Geräte in der Mundhöhle darstellt. Zungenmobilisationsübungen wurden über Probanden hinweg mit einer Rate von 61 % erkannt, wogegen in der Lauterkennung Langvokale die höchste Erkennungsrate erzielten, gefolgt von Kurzvokalen und Konsonanten. Zusammenfassend lässt sich konstatieren, dass das Prinzip der Optopalatographie eine ernstzunehmende Option für die intraorale Erfassung von Zungenbewegungen darstellt, wobei weitere Entwicklungsschritte notwendig sind, welche im Ausblick zusammengefasst sind.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . .

Diagnosis and Treatment of Small Bowel Disorders

Over the last few decades, remarkable progress has been made in understanding the aetiology and pathophysiology of diseases and many new theories emphasize the importance of the small bowel ‘ecosystem’ in the pathogenesis of acute and chronic illness. Emerging factors such as microbiome, stem cells, innate intestinal immunity and the enteric nervous system along with mucosal and endothelial barriers have key role in the development of gastrointestinal and extra-intestinal diseases. Therefore, the small intestine is considered key player in metabolic disease development, including diabetes mellitus, and other diet-related disorders such as celiac and non-celiac enteropathies. Another major field is drug metabolism and its interaction with microbiota. Moreover, the emergence of gut-brain, gut-liver and gut-blood barriers points toward the important role of small intestine in the pathogenesis of common disorders, such as liver disease, hypertension and neurodegenerative disease. However, the small bowel remains an organ that is difficult to fully access and assess and accurate diagnosis often poses a clinical challenge. Eventually, the therapeutic potential remains untapped. Therefore, it is due time to direct our interest towards the small intestine and unravel the interplay between small-bowel and other gastrointestinal (GI) and non-GI related maladies

Generación de una librería RVC – CAL para la etapa de determinación de endmembers en el proceso de análisis de imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar dos de las cuatro fases propias del procesado espectral: reducción dimensional y extracción de endmembers. Cabe mencionar que este trabajo se complementa con el realizado por Raquel Lazcano en su Proyecto Fin de Grado, donde se desarrollan las funciones necesarias para completar las otras dos fases necesarias en la cadena de desmezclado. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Proyecto Fin de Grado y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como los medios y las plataformas que servirán para realizar la división en núcleos y detectar las distintas problemáticas con las que nos podamos encontrar al realizar dicha división. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para componer la cadena de desmezclado y generar la librería; un punto importante en este apartado es la utilización de librerías especializadas en operaciones matriciales complejas, implementadas en C++. Tras explicar el método utilizado, se exponen los resultados obtenidos primero por etapas y, posteriormente, con la cadena de procesado completa, implementada en uno o varios núcleos. Por último, se aportan una serie de conclusiones obtenidas tras analizar los distintos algoritmos en cuanto a bondad de resultados, tiempos de procesado y consumo de recursos y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement two of the four stages of the hyperspectral imaging processing chain: dimensionality reduction and endmember extraction. This research is complemented with the research conducted by Raquel Lazcano in her Diploma Project, where she studies the other two stages of the processing chain. The document is divided in several chapters. The first of them introduces the motivation of the Diploma Project and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images and the software and hardware that we will use to parallelize the system and to analyze its performance. Once we have exposed the theoretical bases, we will explain the followed methodology to compose the processing chain and to generate the library; one of the most important issues in this chapter is the use of some C++ libraries specialized in complex matrix operations. At this point, we will expose the results obtained in the individual stage analysis and then, the results of the full processing chain implemented in one or several cores. Finally, we will extract some conclusions related with algorithm behavior, time processing and system performance. In the same way, we propose some future research lines according to the results obtained in this documen