On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)

Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE\u27s embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm

Bounds on RF cooperative localization for video capsule endoscopy

Wireless video capsule endoscopy has been in use for over a decade and it uses radio frequency (RF) signals to transmit approximately fifty five thousands clear pictures of inside the GI tract to the body-mounted sensor array. However, physician has no clue on the exact location of the capsule inside the GI tract to associate it with the pictures showing abnormalities such as bleeding or tumors. It is desirable to use the same RF signal for localization of the VCE as it passes through the human GI tract. In this thesis, we address the accuracy limits of RF localization techniques for VCE localization applications. We present an assessment of the accuracy of cooperative localization of VCE using radio frequency (RF) signals with particular emphasis on localization inside the small intestine. We derive the Cramer-Rao Lower Bound (CRLB) for cooperative location estimators using the received signal strength(RSS) or the time of arrival (TOA) of the RF signal. Our derivations are based on a three-dimension human body model, an existing model for RSS propagation from implant organs to body surface and a TOA ranging error model for the effects of non-homogenity of the human body on TOA of the RF signals. Using models for RSS and TOA errors, we first calculate the 3D CRLB bounds for cooperative localization of the VCE in three major digestive organs in the path of GI tract: the stomach, the small intestine and the large intestine. Then we analyze the performance of localization techniques on a typical path inside the small intestine. Our analysis includes the effects of number of external sensors, the external sensor array topology, number of VCE in cooperation and the random variations in transmit power from the capsule

Anatomical Classification of the Gastrointestinal Tract Using Ensemble Transfer Learning

Endoscopy is a procedure used to visualize disorders of the gastrointestinal (GI) lumen. GI disorders can occur without symptoms, which is why gastroenterologists often recommend routine examinations of the GI tract. It allows a doctor to directly visualize the inside of the GI tract and identify the cause of symptoms, reducing the need for exploratory surgery or other invasive procedures. It can also detect the early stages of GI disorders, such as cancer, enabling prompt treatment that can improve outcomes. Endoscopic examinations generate significant numbers of GI images. Because of this vast amount of endoscopic image data, relying solely on human interpretation can be problematic. Artificial intelligence is gaining popularity in clinical medicine. Assist in medical image analysis and early detection of diseases, help with personalized treatment planning by analyzing a patient’s medical history and genomic data, and be used by surgical robots to improve precision and reduce invasiveness. It enables automated diagnosis, provides physicians with assistance, and may improve performance. One of the significant challenges is defining the specific anatomic locations of GI tract abnormalities. Clinicians can then determine appropriate treatment options, reducing the need for repetitive endoscopy. Due to the difficulty of collecting annotated data, very limited research has been conducted on the localization of anatomical locations by classification of endoscopy images. In this study, we present a classification of GI tract anatomical localization based on transfer learning and ensemble learning. Our approach involves the use of an autoencoder and the Xception model. The autoencoder was initially trained on thousands of unlabeled images, and the encoder then separated and used as a feature extractor. The Xception model was also used as a second model to extract features from the input images. The extracted feature vectors were then concatenated and fed into a Convolutional Neural Network for classification. This combination of models provides a powerful and versatile solution for image classification. By using the encoder as a feature extractor that can transfer the learned knowledge, it is possible to improve learning by allowing the model to focus on more relevant and useful data, which is extremely valuable when there are not enough appropriately labelled data. On the other hand, the Xception model provides additional feature extraction capabilities. Sometimes, one classifier is not enough in machine learning, as it depends on the problem we are trying to solve and the quality and quantity of data available. With ensemble learning, multiple learning networks can work together to create a stronger classifier. The final classification results are obtained by combining the information from both models through the CNN model. This approach demonstrates the potential for combining multiple models to improve the accuracy of image classification tasks in the medical domain. The HyperKvasir dataset is the main dataset used in this study. It contains 4,104 labelled and 99,417 unlabeled images taken at six different locations in the GI tract, including the cecum, ileum, pylorus, rectum, stomach, and Z line. After dataset preprocessing, which includes noise deduction and similarity removal, 871 labelled images remained for the purpose of this study. Our method was more accurate than state-of-the-art studies and had a higher F1 score while categorizing the input images into six different anatomical locations with less than a thousand labelled images. According to the results, feature extraction and ensemble learning increase accuracy by 5%, and a comparison with existing methods using the same dataset indicate improved performance and reduced cross entropy loss. The proposed method can therefore be used in the classification of endoscopy images

Human exposure to electromagnetic fields from WLANs and WBANs in the 2.4 GHz band

226 p.En los últimos años, el masivo crecimiento de las comunicaciones inalámbricas ha incrementado la preocupación acerca de la exposición humana a los campos electromagnéticos debido a los posibles efectos sobre la salud. Esta tesis surge de la necesidad de proporcionar información acerca de este tipo de exposición desde un punto de vista técnico. Por una parte, se han estudiado los niveles de exposición causados por señales WiFi, para lo cual ha sido necesario establecer un procedimiento de medida adecuado para tomar muestras de estas emisiones. Además, se han llevado a cabo campañas de medida para evaluar la exposición a señales WiFi y su variabilidad en el interior de un entorno público. Por otra parte, se ha analizado la potencia absorbida por el cuerpo humano a causa de los novedosos dispositivos wearables. Se han implementado dos antenas de este tipo, apropiadas para dispositivos wearables, se ha analizado detalladamente la exposición debida a estos aparatos y finalmente se han comparado los niveles de exposición producidos por estas antenas y por las señales WiFi

Somatostatin Receptor Scintigraphy in Medullary Thyroid Cancer

Medullary thyroid cancer (MTC) is a neuroendocrine tumor originating from the calcitonin‐secreting C cells. Surgery, consisting of a total thyroidectomy and an extensive lymph node dissection, is the only effective treatment in MTC; however, metastases are frequently found in the regional cervical lymph. The biochemical marker for MTC is calcitonin, and this is frequently used for the detection of persistent/residual/metastatic tumor. The value of 111In‐labeled somatostatin receptor scintigraphy (SRS) in patients with MTC is limited, with sensitivity ranging between 0 and 75%. Other scintigraphic imaging techniques such as 18F‐FDG PET, 18F‐DOPA PET, and PET imaging with 68Ga‐labeled DOTA peptides combined with CT imaging are upcoming. Treatment of patients with metastatic disease with the current available somatostatin analogues, octreotide and lanreotide, does not seem to have an effect on survival but may be considered to control flushing and diarrhea in some patients. Experience with peptide receptor radionuclide therapy is limited in this patient group and disappointing. New therapies in the treatment of metastatic MTC use target tyrosine kinase receptors inhibitors belonging to the same family group of proteins as RET

Other Radiopharmaceuticals for Imaging GEP‐NET

In GEP‐NETs, especially the catecholamine and serotonin biosynthetic pathways are upregulated. Therefore, increased biosynthesis of these specific amines in GEP‐NETs enables imaging with specific amine precursors. For the catecholamine pathway, 6‐18F ‐l‐3,4‐dihydroxyphenylalanine (18F‐DOPA) is available, while for the serotonin pathway, carbon‐11‐labeled 5‐hydroxy‐l‐tryptophan ([11C]‐5‐HTP) is available as tracer. 11C‐5‐HTP PET and 18F‐DOPA PET are excellent functional imaging techniques for evaluating patients with proven pancreatic islet cell tumors and carcinoids. For both tracers, the combination with CT further improves the detection rate of NET, which shows that performing PET scans with these tracers in PET/CT scanners is beneficial for patients.Since well‐differentiated GEP‐NETs generally have a low glucose metabolism, 18F‐fluorodexyglucose (18F‐FDG) PET scanning has limited value for the primary staging of patients with well‐differentiated GEP‐NETs. However, in patients with rapidly progressive disease, dedifferentiation of GEP‐NET tumors can lead to a higher glucose metabolism in tumor cells. In these patients, 18F‐FDG PET can be of benefit for tumor staging. Also, 18F‐FDG PET can be of value when other malignancies are suspected in patients with GEP‐NETs, since these patients experience a higher incidence of these malignancies compared to the general population.Nowadays, (GEP)‐NETs can also be imaged with 68Ga‐labeled analogues of somatostatin, which are also PET tracers. Advantages of 68Ga‐labeled somatostatin analogues are the relatively easy generator‐based synthesis and the possibility to evaluate whether peptide (somatostatin) receptor radionuclide therapy (PRRT) for NETs can be considered

Human exposure to electromagnetic fields from WLANs and WBANs in the 2.4 GHz band

226 p.En los últimos años, el masivo crecimiento de las comunicaciones inalámbricas ha incrementado la preocupación acerca de la exposición humana a los campos electromagnéticos debido a los posibles efectos sobre la salud. Esta tesis surge de la necesidad de proporcionar información acerca de este tipo de exposición desde un punto de vista técnico. Por una parte, se han estudiado los niveles de exposición causados por señales WiFi, para lo cual ha sido necesario establecer un procedimiento de medida adecuado para tomar muestras de estas emisiones. Además, se han llevado a cabo campañas de medida para evaluar la exposición a señales WiFi y su variabilidad en el interior de un entorno público. Por otra parte, se ha analizado la potencia absorbida por el cuerpo humano a causa de los novedosos dispositivos wearables. Se han implementado dos antenas de este tipo, apropiadas para dispositivos wearables, se ha analizado detalladamente la exposición debida a estos aparatos y finalmente se han comparado los niveles de exposición producidos por estas antenas y por las señales WiFi

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Recent Advances in Minimally Invasive Surgery

Minimally invasive surgery has become a common term in visceral as well as gynecologic surgery. It has almost evolved into its own surgical speciality over the past 20 years. Today, being firmly established in every subspeciality of visceral surgery, it is now no longer a distinct skillset, but a fixed part of the armamentarium of surgical options available. In every indication, the advantages of a minimally invasive approach include reduced intraoperative blood loss, less postoperative pain, and shorter rehabilitation times, as well as a marked reduction of overall and surgical postoperative morbidity. In the advent of modern oncologic treatment algorithms, these effects not only lower the immediate impact that an operation has on the patient, but also become important key steps in reducing the side-effects of surgery. Thus, they enable surgery to become a module in modern multi-disciplinary cancer treatment, which blends into multimodular treatment options at different times and prolongs and widens the possibilities available to cancer patients. In this quickly changing environment, the requirement to learn and refine not only open surgical but also different minimally invasive techniques on high levels deeply impact modern surgical training pathways. The use of modern elearning tools and new and praxis-based surgical training possibilities have been readily integrated into modern surgical education,which persists throughout the whole surgical career of modern gynecologic and visceral surgery specialists

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on