1,067 research outputs found
A Colour Wheel to Rule them All: Analysing Colour & Geometry in Medical Microscopy
Personalized medicine is a rapidly growing field in healthcare that aims to customize
medical treatments and preventive measures based on each patient’s unique characteristics,
such as their genes, environment, and lifestyle factors. This approach
acknowledges that people with the same medical condition may respond differently
to therapies and seeks to optimize patient outcomes while minimizing the risk
of adverse effects.
To achieve these goals, personalized medicine relies on advanced technologies,
such as genomics, proteomics, metabolomics, and medical imaging. Digital
histopathology, a crucial aspect of medical imaging, provides clinicians with valuable
insights into tissue structure and function at the cellular and molecular levels. By
analyzing small tissue samples obtained through minimally invasive techniques, such
as biopsy or aspirate, doctors can gather extensive data to evaluate potential diagnoses
and clinical decisions. However, digital analysis of histology images presents
unique challenges, including the loss of 3D information and stain variability, which
is further complicated by sample variability. Limited access to data exacerbates
these challenges, making it difficult to develop accurate computational models for
research and clinical use in digital histology.
Deep learning (DL) algorithms have shown significant potential for improving the
accuracy of Computer-Aided Diagnosis (CAD) and personalized treatment models,
particularly in medical microscopy. However, factors such as limited generability,
lack of interpretability, and bias sometimes hinder their clinical impact. Furthermore,
the inherent variability of histology images complicates the development of robust DL
methods. Thus, this thesis focuses on developing new tools to address these issues.
Our essential objective is to create transparent, accessible, and efficient methods
based on classical principles from various disciplines, including histology, medical
imaging, mathematics, and art, to tackle microscopy image registration and colour
analysis successfully. These methods can contribute significantly to the advancement
of personalized medicine, particularly in studying the tumour microenvironment
for diagnosis and therapy research.
First, we introduce a novel automatic method for colour analysis and non-rigid
histology registration, enabling the study of heterogeneity morphology in tumour
biopsies. This method achieves accurate tissue cut registration, drastically reducing
landmark distance and excellent border overlap. Second, we introduce ABANICCO, a novel colour analysis method that combines
geometric analysis, colour theory, fuzzy colour spaces, and multi-label systems
for automatically classifying pixels into a set of conventional colour categories.
ABANICCO outperforms benchmark methods in accuracy and simplicity. It is
computationally straightforward, making it useful in scenarios involving changing
objects, limited data, unclear boundaries, or when users lack prior knowledge of
the image or colour theory. Moreover, results can be modified to match each
particular task.
Third, we apply the acquired knowledge to create a novel pipeline of rigid
histology registration and ABANICCO colour analysis for the in-depth study of
triple-negative breast cancer biopsies. The resulting heterogeneity map and tumour
score provide valuable insights into the composition and behaviour of the tumour,
informing clinical decision-making and guiding treatment strategies.
Finally, we consolidate the developed ideas into an efficient pipeline for tissue
reconstruction and multi-modality data integration on Tuberculosis infection data.
This enables accurate element distribution analysis to understand better interactions
between bacteria, host cells, and the immune system during the course of infection.
The methods proposed in this thesis represent a transparent approach to computational
pathology, addressing the needs of medical microscopy registration and
colour analysis while bridging the gap between clinical practice and computational
research. Moreover, our contributions can help develop and train better, more
robust DL methods.En una época en la que la medicina personalizada está revolucionando la asistencia
sanitaria, cada vez es más importante adaptar los tratamientos y las medidas
preventivas a la composición genética, el entorno y el estilo de vida de cada
paciente. Mediante el empleo de tecnologías avanzadas, como la genómica, la
proteómica, la metabolómica y la imagen médica, la medicina personalizada se
esfuerza por racionalizar el tratamiento para mejorar los resultados y reducir
los efectos secundarios.
La microscopía médica, un aspecto crucial de la medicina personalizada, permite
a los médicos recopilar y analizar grandes cantidades de datos a partir de pequeñas
muestras de tejido. Esto es especialmente relevante en oncología, donde las terapias
contra el cáncer se pueden optimizar en función de la apariencia tisular específica de
cada tumor. La patología computacional, un subcampo de la visión por ordenador,
trata de crear algoritmos para el análisis digital de biopsias. Sin embargo, antes de
que un ordenador pueda analizar imágenes de microscopía médica, hay que seguir
varios pasos para conseguir las imágenes de las muestras.
La primera etapa consiste en recoger y preparar una muestra de tejido del
paciente. Para que esta pueda observarse fácilmente al microscopio, se corta en
secciones ultrafinas. Sin embargo, este delicado procedimiento no está exento de
dificultades. Los frágiles tejidos pueden distorsionarse, desgarrarse o agujerearse,
poniendo en peligro la integridad general de la muestra.
Una vez que el tejido está debidamente preparado, suele tratarse con tintes de
colores característicos. Estos tintes acentúan diferentes tipos de células y tejidos
con colores específicos, lo que facilita a los profesionales médicos la identificación
de características particulares. Sin embargo, esta mejora en visualización tiene
un alto coste. En ocasiones, los tintes pueden dificultar el análisis informático
de las imágenes al mezclarse de forma inadecuada, traspasarse al fondo o alterar
el contraste entre los distintos elementos.
El último paso del proceso consiste en digitalizar la muestra. Se toman imágenes
de alta resolución del tejido con distintos aumentos, lo que permite su análisis por
ordenador. Esta etapa también tiene sus obstáculos. Factores como una calibración
incorrecta de la cámara o unas condiciones de iluminación inadecuadas pueden
distorsionar o hacer borrosas las imágenes. Además, las imágenes de porta completo
obtenidas so de tamaño considerable, complicando aún más el análisis. En general, si bien la preparación, la tinción y la digitalización de las muestras
de microscopía médica son fundamentales para el análisis digital, cada uno de estos
pasos puede introducir retos adicionales que deben abordarse para garantizar un
análisis preciso. Además, convertir un volumen de tejido completo en unas pocas
secciones teñidas reduce drásticamente la información 3D disponible e introduce
una gran incertidumbre.
Las soluciones de aprendizaje profundo (deep learning, DL) son muy prometedoras
en el ámbito de la medicina personalizada, pero su impacto clínico a veces se
ve obstaculizado por factores como la limitada generalizabilidad, el sobreajuste, la
opacidad y la falta de interpretabilidad, además de las preocupaciones éticas y en
algunos casos, los incentivos privados. Por otro lado, la variabilidad de las imágenes
histológicas complica el desarrollo de métodos robustos de DL. Para superar estos
retos, esta tesis presenta una serie de métodos altamente robustos e interpretables
basados en principios clásicos de histología, imagen médica, matemáticas y arte,
para alinear secciones de microscopía y analizar sus colores.
Nuestra primera contribución es ABANICCO, un innovador método de análisis
de color que ofrece una segmentación de colores objectiva y no supervisada y permite
su posterior refinamiento mediante herramientas fáciles de usar. Se ha demostrado
que la precisión y la eficacia de ABANICCO son superiores a las de los métodos
existentes de clasificación y segmentación del color, e incluso destaca en la detección
y segmentación de objetos completos. ABANICCO puede aplicarse a imágenes
de microscopía para detectar áreas teñidas para la cuantificación de biopsias, un
aspecto crucial de la investigación de cáncer.
La segunda contribución es un método automático y no supervisado de segmentación
de tejidos que identifica y elimina el fondo y los artefactos de las
imágenes de microscopía, mejorando así el rendimiento de técnicas más sofisticadas
de análisis de imagen. Este método es robusto frente a diversas imágenes, tinciones
y protocolos de adquisición, y no requiere entrenamiento.
La tercera contribución consiste en el desarrollo de métodos novedosos para
registrar imágenes histopatológicas de forma eficaz, logrando el equilibrio adecuado
entre un registro preciso y la preservación de la morfología local, en función de
la aplicación prevista.
Como cuarta contribución, los tres métodos mencionados se combinan para
crear procedimientos eficientes para la integración completa de datos volumétricos,
creando visualizaciones altamente interpretables de toda la información presente en
secciones consecutivas de biopsia de tejidos. Esta integración de datos puede tener
una gran repercusión en el diagnóstico y el tratamiento de diversas enfermedades,
en particular el cáncer de mama, al permitir la detección precoz, la realización
de pruebas clínicas precisas, la selección eficaz de tratamientos y la mejora en la
comunicación el compromiso con los pacientes. Por último, aplicamos nuestros hallazgos a la integración multimodal de datos y
la reconstrucción de tejidos para el análisis preciso de la distribución de elementos
químicos en tuberculosis, lo que arroja luz sobre las complejas interacciones entre
las bacterias, las células huésped y el sistema inmunitario durante la infección
tuberculosa. Este método también aborda problemas como el daño por adquisición,
típico de muchas modalidades de imagen.
En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por
ordenador en el registro de microscopía médica y el análisis de color para abordar
los retos únicos de este campo, haciendo hincapié en la visualización eficaz y fácil de
datos complejos. Aspiramos a seguir perfeccionando nuestro trabajo con una amplia
validación técnica y un mejor análisis de los datos. Los métodos presentados en esta
tesis se caracterizan por su claridad, accesibilidad, visualización eficaz de los datos,
objetividad y transparencia. Estas características los hacen perfectos para tender
puentes robustos entre los investigadores de inteligencia artificial y los clínicos e
impulsar así la patología computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo Ríos Muñoz.- Vocal: Estíbaliz Gómez de Marisca
Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.
In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland
Image Guided Biodistribution and Pharmacokinetic Studies of Theranostics
Image guided technique is playing an increasingly important role in the investigation of the biodistribution and pharmacokinetics of drugs or drug delivery systems in various diseases, especially cancers. Besides anatomical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), molecular imaging strategy including optical imaging, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) will facilitate the localization and quantization of radioisotope or optical probe la-beled nanoparticle delivery systems in the category of theranostics. The quantitative meas-urement of the bio-distribution and pharmacokinetics of theranostics in the fields of new drug/probe development, diagnosis and treatment process monitoring as well as tracking the brain-blood-barrier (BBB) breaking through by high sensitive imaging method, and the ap-plications of the representative imaging modalities are summarized in this review
Making modality: transmodal composing in a digital media studio.
The multiple media that exist for communication have historically been theorized as possessing different available means for persuasion and meaning-making. The exigence of these means has been the object of theoretical debate that ranges from cultural studies, language studies, semiology, and philosophies of the mind. This dissertation contributes to such debates by sharing the results of an ethnographically informed study of multimedia composing in a digital media studio. Drawing from Cultural Historical Activity Theory and theories of enactive perception, I analyze the organizational and infrastructural design of a media studio as well as the activity of composer/designers working in said studio. Throughout this analysis I find that implicit in the organization and infrastructure of the media studio is an ethos of conceptualizing communication technology as a legitimizing force. Such an ethos is troubled by my analysis of composer/designers working in the studio, whose activities do not seek outside legitimization but instead contribute to the media milieu. Following these analyses, I conclude that media’s means for persuasion and meaning-making emerge from local practices of communication and design. Finally, I provide a framework for studying the emergence of such means
A Cyberpunk 2077 perspective on the prediction and understanding of future technology
Science fiction and video games have long served as valuable tools for
envisioning and inspiring future technological advancements. This position
paper investigates the potential of Cyberpunk 2077, a popular science fiction
video game, to shed light on the future of technology, particularly in the
areas of artificial intelligence, edge computing, augmented humans, and
biotechnology. By analyzing the game's portrayal of these technologies and
their implications, we aim to understand the possibilities and challenges that
lie ahead. We discuss key themes such as neurolink and brain-computer
interfaces, multimodal recording systems, virtual and simulated reality,
digital representation of the physical world, augmented and AI-based home
appliances, smart clothing, and autonomous vehicles. The paper highlights the
importance of designing technologies that can coexist with existing preferences
and systems, considering the uneven adoption of new technologies. Through this
exploration, we emphasize the potential of science fiction and video games like
Cyberpunk 2077 as tools for guiding future technological advancements and
shaping public perception of emerging innovations.Comment: 12 pages, 7 figure
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is
crucial for research and clinical applications. Advances in computing, deep
learning, and availability of large WSI datasets have revolutionised WSI
analysis. Therefore, the current state-of-the-art in WSI registration is
unclear. To address this, we conducted the ACROBAT challenge, based on the
largest WSI registration dataset to date, including 4,212 WSIs from 1,152
breast cancer patients. The challenge objective was to align WSIs of tissue
that was stained with routine diagnostic immunohistochemistry to its
H&E-stained counterpart. We compare the performance of eight WSI registration
algorithms, including an investigation of the impact of different WSI
properties and clinical covariates. We find that conceptually distinct WSI
registration methods can lead to highly accurate registration performances and
identify covariates that impact performances across methods. These results
establish the current state-of-the-art in WSI registration and guide
researchers in selecting and developing methods
Innovations in Metastatic Brain Tumor Treatment
Metastatic brain tumors (MBTs) are the most common intracranial tumor and occur in up to 40% of patients with certain cancer diagnoses. The most common and frequent primary locations are cancers originating from the lung, breast, kidney, gastrointestinal tract or skin, and also may arising from any part of the body. Treatment for brain metastasis management includes surgery, whole brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), and chemotherapy. Standard treatment for MBTs includes surgery and SRS which offer the best outcomes, while the WBRT is still an important treatment option for patients who cannot tolerate surgery and SRS or patients with multiple brain metastases. Newer approaches such as immunotherapy and molecularly targeted therapy (e.g., small molecules and monoclonal antibodies) are currently being evaluated for the treatment of MBTs. In this chapter, we will review current available treatments for MBTs and discuss treatments that are undergoing active investigation
Plasmonic artificial virus nano-particles for probing virus-host cell interactions
Targeting of key events in viral infection pathways creates opportunities for virus disease prevention and therapy. Nanoparticles with well-defined surfaces are promising tools for the direct visualization of biological processes and for interrogating virus behavior that is usually determined by the synergistic interplay of multiple factors and involves various transient signaling steps. Smart nanoparticles mimicking enveloped viral particles are thus developed and tested in this work with the aim to de-couple key steps in human immune-deficiency virus HIV-1 trans-infection with an engineerable viral model system.
Uni-lamellar liposomes resemble biological lipid bilayer membrane structures with tunable particle size, surface charge, and composition. Pretreatment with ganglioside-GM3-containing liposomes inhibited the binding of HIV-1 by dendritic cells, indicating an essential role for GM3 in virus binding. To equip the liposome based model systems with strong non bleaching optical properties, the membranes were in the next step assembled around noble metal nanoparticle core. Noble metal nanoparticles with a size of 20nm-100nm have extraordinarily large scattering cross-sections and enable prolonged tracking of even individual particles with high temporal and spatial resolutions. The plasmon resonance peak of near-field coupled gold nanoparticles red-shifts within decreasing interparticle separation. The distance dependent optical properties of noble metal nanoparticles were utilized for characterizing clustering levels of breast cancer cell marker protein CD24 and CD44 on immortalized cancer cell lines. These encouraging results supported the choice of gold nanoparticles as core for multi-modal artificial virus nanoparticles.
Artificial virus nanoparticles combine the biological versatility of a self-assembled membrane with the unique optical properties of a nanoparticle core. We developed these hybrid materials specifically for the purpose of elucidating key steps of the glycoprotein independent binding and uptake of HIV-1 during trans-infection. Systematic validation experiments revealed that GM3 containing artificial virus nanoparticles (AVNs) recapitulate the initial capture and uptake of viruses by sialoadhesin CD169 presenting cells. The AVNs also reproduced the tendency of the virus to re-distribute into confined cluster spots in cell peripheral areas. Upon contact formation between T cell and DC, the AVNs developed a polarized distribution in which they enriched at the interface between DC and CD4+ T cells. The multimodality of the AVNs was instrumental in determining the detailed location and kinetics of the nanoparticles during the trans-infection process, proving the AVN system to be a unique model system to address key mechanistic questions in the infection pathway of enveloped virus particles
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