641 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations
Ultrasound (US) imaging is widely used for biometric measurement and
diagnosis of internal organs due to the advantages of being real-time and
radiation-free. However, due to high inter-operator variability, resulting
images highly depend on operators' experience. In this work, an intelligent
robotic sonographer is proposed to autonomously "explore" target anatomies and
navigate a US probe to a relevant 2D plane by learning from expert. The
underlying high-level physiological knowledge from experts is inferred by a
neural reward function, using a ranked pairwise image comparisons approach in a
self-supervised fashion. This process can be referred to as understanding the
"language of sonography". Considering the generalization capability to overcome
inter-patient variations, mutual information is estimated by a network to
explicitly extract the task-related and domain features in latent space.
Besides, a Gaussian distribution-based filter is developed to automatically
evaluate and take the quality of the expert's demonstrations into account. The
robotic localization is carried out in coarse-to-fine mode based on the
predicted reward associated to B-mode images. To demonstrate the performance of
the proposed approach, representative experiments for the "line" target and
"point" target are performed on vascular phantom and two ex-vivo animal organ
phantoms (chicken heart and lamb kidney), respectively. The results
demonstrated that the proposed advanced framework can robustly work on
different kinds of known and unseen phantoms
Application of Advanced MRI to Fetal Medicine and Surgery
Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution.
This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development.
I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death
Generalizable deep learning based medical image segmentation
Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications.
To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques.
In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain.
For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios.
In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation.
In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method.
Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces
Psychological and care impact of the daily use of a pediatric gait exoskeleton in children with spinal muscular atrophy
Introducción: La Atrofia Muscular Espinal Tipo II, es una enfermedad neurodegenerativa de origen genético que cursa con debilidad muscular y provoca deterioro motor e incapacidad para caminar en los niños. Se relaciona con graves problemas respiratorios, musculoesqueléticos, gastrointestinales y otros de salud y cuidado. Los exoesqueletos robóticos de miembros inferiores son dispositivos médicos que ayudan a la marcha de pacientes que no pueden caminar. Nuestro objetivo fue evaluar el impacto en la dimensión psicológica y de autocuidado derivado del uso del exoesqueleto ATLAS en el hogar en niños con Atrofia Muscular Espinal Tipo II. Metodología: tres niños con Atrofia Muscular Espinal Tipo II utilizaron el exoesqueleto en casa cinco días a la semana durante un período de dos meses para caminar y realizar actividades. Se realizó una evaluación del autocuidado de enfermería antes y durante el uso del dispositivo para evaluar los cambios en los resultados del autocuidado y los diagnósticos de enfermería. Se realizaron entrevistas en profundidad y semiestructuradas, además de la observación durante las sesiones, para evaluar el impacto de la experiencia en la dimensión psicológica de los participantes. Resultados: el uso del exoesqueleto produjo cambios en los condicionantes básicos de los niños y una mejora en los organismos de autocuidado. También aparecieron nuevas demandas de autocuidado. Tres de los diez diagnósticos de enfermería fueron resueltos. Asimismo, los niños mostraron una buena tolerancia a la actividad además de una mejora funcional evaluada en el tercer participante. Los niños y los cuidadores principales valoraron la experiencia como positiva y significativa. Los niños tenían una mayoría de emociones positivas, y se incrementó su autonomía y comportamiento social y exploratorio. Conclusiones: la tecnología del exoesqueleto podría considerarse como un nuevo recurso para el cuidado de niños con enfermedades neuromusculares. Su uso tuvo un impacto positivo tanto en las variables de autocuidado como en la dimensión psicológica de tres niños con atrofia muscular espinal tipo II. Aunque este estudio aporta ya evidencia, más estudios sobre el tema aportarían un mayor conocimiento
Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation
Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology.
Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison.
The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%.
In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing
Introduction to Human Biology
This OER is intended as a textbook for a one semester introductory course in Human Anatomy and Physiology for non-science majors. It covers the major topics typically covered in A&P, but in a simplified, easier to understand manner. This textbook aims to educate students interested in lower-level health careers and non-science majors without the intimidating detail found in current textbooks. Text and images were created to be more accessible for these student populations
Electronic Devices for the Combination of Electrically Controlled Drug Release, Electrostimulation, and Optogenetic Stimulation for Nerve Tissue Regeneration
[ES] La capacidad de las células madre para proliferar formando distintas células especializadas les otorga la potencialidad de servir de base para terapias efectivas para patologías cuyo tratamiento era inimaginable hasta hace apenas dos décadas. Sin embargo, esta capacidad se encuentra mediada por estímulos fisiológicos, químicos, y eléctricos, específicos y complejos, que dificultan su traslación a la rutina clínica. Por ello, las células madre representan un campo de estudio en el que se invierten amplios esfuerzos por parte de la comunidad científica.
En el ámbito de la regeneración nerviosa, para modular su desarrollo y diferenciación el tratamiento farmacológico, la electroestimulación, y la estimulación optogenética son técnicas que están consiguiendo prometedores resultados. Es por ello por lo que en la presente tesis se ha desarrollado un conjunto de sistemas electrónicos para permitir la aplicación combinada de estas técnicas in vitro, con perspectiva a su aplicación in vivo.
Hemos diseñado una novedosa tecnología para la liberación eléctricamente controlada de fármacos. Esta tecnología está basada en nanopartículas de sílice mesoporosa y puertas moleculares de bipiridina-heparina. Las puertas moleculares son electroquímicamente reactivas, y encierran los fármacos en el interior de las nanopartículas, liberándolos ante un estímulo eléctrico. Hemos caracterizado esta tecnología, y la hemos validado mediante la liberación controlada de rodamina en cultivos celulares de HeLa. Para la combinación de liberación controlada de fármacos y electroestimulación hemos desarrollado dispositivos que permiten aplicar los estímulos eléctricos de forma configurable desde una interfaz gráfica de usuario. Además, hemos diseñado un módulo de expansión que permite multiplexar las señales eléctricas a diferentes cultivos celulares.
Además, hemos diseñado un dispositivo de estimulación optogenética. Este tipo de estimulación consiste en la modificación genética de las células para que sean sensibles a la radiación lumínica de determinada longitud de onda. En el ámbito de la regeneración de tejido mediante células precursoras neurales, es de interés poder inducir ondas de calcio, favoreciendo su diferenciación en neuronas y la formación de circuitos sinápticos. El dispositivo diseñado permite obtener imágenes en tiempo real mediante microscopía confocal de las respuestas transitorias de las células al ser irradiadas. El dispositivo se ha validado irradiando neuronas modificadas con luz pulsada de 100 ms. También hemos diseñado un dispositivo electrónico complementario de medida de irradiancia con el doble fin de permitir la calibración del equipo de irradiancia y medir la irradiancia en tiempo real durante los experimentos in vitro.
Los resultados del uso de los bioactuadores en procesos complejos y dinámicos, como la regeneración de tejido nervioso, son limitados en lazo abierto. Uno de los principales aspectos analizados es el desarrollo de biosensores que permitiesen la cuantización de ciertas biomoléculas para ajustar la estimulación suministrada en tiempo real. Por ejemplo, la segregación de serotonina es una respuesta identificada en la elongación de células precursoras neurales, pero hay otras biomoléculas de interés para la implementación de un control en lazo cerrado. Entre las tecnologías en el estado del arte, los biosensores basados en transistores de efecto de campo (FET) funcionalizados con aptámeros son realmente prometedores para esta aplicación. Sin embargo, esta tecnología no permitía la medición simultánea de más de una biomolécula objetivo en un volumen reducido debido a las interferencias entre los distintos FETs, cuyos terminales se encuentran inmersos en la solución. Por ello, hemos desarrollado instrumentación electrónica capaz de medir simultáneamente varios de estos biosensores, y la hemos validado mediante la medición simultánea de pH y la detección preliminar de serotonina y glutamato.[CA] La capacitat de les cèl·lules mare per a proliferar formant diferents cèl·lules especialitzades els atorga la potencialitat de servir de base per a teràpies efectives per a patologies el tractament de les quals era inimaginable fins fa a penes dues dècades. No obstant això, aquesta capacitat es troba mediada per estímuls fisiològics, químics, i elèctrics, específics i complexos, que dificulten la seua translació a la rutina clínica. Per això, les cèl·lules mare representen un camp d'estudi en el qual s'inverteixen amplis esforços per part de la comunitat científica.
En l'àmbit de la regeneració nerviosa, per a modular el seu desenvolupament i diferenciació el tractament farmacològic, l'electroestimulació, i l'estimulació optogenética són tècniques que estan aconseguint prometedors resultats. És per això que en la present tesi s'ha desenvolupat un conjunt de sistemes electrònics per a permetre l'aplicació combinada d'aquestes tècniques in vitro, amb perspectiva a la seua aplicació in vivo.
Hem dissenyat una nova tecnologia per a l'alliberament elèctricament controlat de fàrmacs. Aquesta tecnologia està basada en nanopartícules de sílice mesoporosa i portes moleculars de bipiridina-heparina. Les portes moleculars són electroquímicament reactives, i tanquen els fàrmacs a l'interior de les nanopartícules, alliberant-los davant un estímul elèctric. Hem caracteritzat aquesta tecnologia, i l'hem validada mitjançant l'alliberament controlat de rodamina en cultius cel·lulars de HeLa. Per a la combinació d'alliberament controlat de fàrmacs i electroestimulació hem desenvolupat dispositius que permeten aplicar els estímuls elèctrics de manera configurable des d'una interfície gràfica d'usuari. A més, hem dissenyat un mòdul d'expansió que permet multiplexar els senyals elèctrics a diferents cultius cel·lulars.
A més, hem dissenyat un dispositiu d'estimulació optogenètica. Aquest tipus d'estimulació consisteix en la modificació genètica de les cèl·lules perquè siguen sensibles a la radiació lumínica de determinada longitud d'ona. En l'àmbit de la regeneració de teixit mitjançant cèl·lules precursores neurals, és d'interés poder induir ones de calci, afavorint la seua diferenciació en neurones i la formació de circuits sinàptics. El dispositiu dissenyat permet obtindré imatges en temps real mitjançant microscòpia confocal de les respostes transitòries de les cèl·lules en ser irradiades. El dispositiu s'ha validat irradiant neurones modificades amb llum polsada de 100 ms. També hem dissenyat un dispositiu electrònic complementari de mesura d'irradiància amb el doble fi de permetre el calibratge de l'equip d'irradiància i mesurar la irradiància en temps real durant els experiments in vitro.
Els resultats de l'ús dels bioactuadors en processos complexos i dinàmics, com la regeneració de teixit nerviós, són limitats en llaç obert. Un dels principals aspectes analitzats és el desenvolupament de biosensors que permeteren la quantització de certes biomolècules per a ajustar l'estimulació subministrada en temps real. Per exemple, la segregació de serotonina és una resposta identificada amb l'elongació de les cèl·lules precursores neurals, però hi ha altres biomolècules d'interés per a la implementació d'un control en llaç tancat. Entre les tecnologies en l'estat de l'art, els biosensors basats en transistors d'efecte de camp (FET) funcionalitzats amb aptàmers són realment prometedors per a aquesta aplicació. No obstant això, aquesta tecnologia no permetia el mesurament simultani de més d'una biomolècula objectiu en un volum reduït a causa de les interferències entre els diferents FETs, els terminals dels quals es troben immersos en la solució. Per això, hem desenvolupat instrumentació electrònica capaç de mesurar simultàniament diversos d'aquests biosensors i els hem validat amb mesurament simultani del pH i la detecció preliminar de serotonina i glutamat.[EN] The stem cells' ability to proliferate to form different specialized cells gives them the potential to serve as the basis for effective therapies for pathologies whose treatment was unimaginable until just two decades ago. However, this capacity is mediated by specific and complex physiological, chemical, and electrical stimuli that complicate their translation to clinical routine. For this reason, stem cells represent a field of study in which the scientific community is investing a great deal of effort.
In the field of nerve regeneration, to modulate their development and differentiation, pharmacological treatment, electrostimulation, and optogenetic stimulation are techniques that are achieving promising results. For this reason, we have developed a set of electronic systems to allow the combined application of these techniques in vitro, with a view to their application in vivo.
We have designed a novel technology for the electrically controlled release of drugs. This technology is based on mesoporous silica nanoparticles and bipyridine-heparin molecular gates. The molecular gates are electrochemically reactive and entrap the drugs inside the nanoparticles, releasing them upon electrical stimulus. We have characterized this technology and validated it by controlled release of rhodamine in HeLa cell cultures. For combining electrostimulation and controlled drug release we have developed devices that allow applying the different electrical stimuli in a configurable way from a graphical user interface. In addition, we have designed an expansion module that allows multiplexing electrical signals to different cell cultures.
In addition, we have designed an optogenetic stimulation device. This type of stimulation consists of genetically modifying cells to make them sensitive to light radiation of a specific wavelength. In tissue regeneration using neural precursor cells, it is interesting to be able to induce calcium waves, favoring the cell differentiation into neurons and the formation of synaptic circuits. The designed device enable the obtention of real-time images through confocal microscopy of the transient responses of cells upon irradiation. The device has been validated by irradiating modified neurons with 100 ms pulsed light stimulation. We have also designed a complementary electronic irradiance measurement device to allow calibration of the irradiator equipment and measuring irradiance in real time during in vitro experiments.
The results of using bioactuators in complex and dynamic processes, such as nerve tissue regeneration, are limited in an open loop. One of the main aspects analyzed is the development of biosensors that would allow quantifying of specific biomolecules to adjust the stimulation provided in real time. For instance, serotonin secretion is an identified response of neural precursor cells elongation, among other biomolecules of interest for the implementation of a closed-loop control. Among the state-of-the-art technologies, biosensors based on field effect transistors (FETs) functionalized with aptamers are promising for this application. However, this technology did not allow the simultaneous measurement of more than one target biomolecule in a small volume due to interferences between the different FETs, whose terminals are immersed in the solution. This is why we have developed electronic instrumentation capable of simultaneously measuring several of these biosensors, and we have validated it with the simultaneous pH measurement and the preliminary detection of serotonin and glutamate.Monreal Trigo, J. (2023). Electronic Devices for the Combination of Electrically Controlled Drug Release, Electrostimulation, and Optogenetic Stimulation for Nerve Tissue Regeneration [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19384
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