983 research outputs found
Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System
Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)
The Discrete Analysis of the Tissue Biopsy Images with Metamaterial Formalization:Identifying Tumor Locus
Herein, we develop an enhanced and automated methodology for detection of the tumour cells in fixed biopsy samples. Metamaterial formalism (MMF) approach allowing recognition of tumour areas in tissue samples is enhanced by providing an advanced technique to digitize mouse biopsy images. Thus, a colour-based segmentation technique based on the K-means clustering method is used allowing for a precise segmentation of the cells composing the biological tissue sample. Errors occurring at the tissue digitization steps are detected by applying MMF. Doing so, we end up with the robust, fully automated approach with no needs of the human intervention, ready for the clinical applications. The proposed methodology consists of three major steps, i. e. digitization of the biopsy image, analysis of the biopsy image, modelling of the disordered metamaterial. It is worthwhile mentioning, that the technique under consideration allows for the cancer stage detection. Moreover, early stage cancer diagnosis is possible by applying MMF
Deep Learning-based Computer-Aided Diagnosis systems: a contribution to prostate cancer detection in histopathological images
In this work, novel computer-aided diagnosis systems for medical image
analysis focusing on prostate cancer are proposed and implemented. First, the
histopathology of prostate cancer was studied, along with the Gleason Grading
System, which measures the aggressiveness of a tumor through different patterns
with the purpose of driving therapies dealing with this disease. Furthermore,
a study of Deep Learning techniques, particularly focusing on neural networks
applied to medical image analysis, was conducted.
Based on these studies, a Deep Learning-based system to detect malignant
regions in gigapixel-size whole-slide prostate cancer tissue images was proposed
and developed, which is able to report spatial information of the malignant
areas. This solution was evaluated in terms of performance and execution time,
obtaining promising results when compared to other state-of-the-art methods.
Since the implemented system locates malignant regions within the image
without providing a global class, a customWide & Deep network was developed
to report a slide-level label per image. The proposed system provides a
fast screening method for analyzing histopathological images. Next, a neural
network was proposed to assign a specific Gleason pattern to the malignant
areas of the tissue. Finally, with the purpose of developing a global computeraided
diagnosis system for prostate cancer detection and classification, the
three aforementioned subsystems were combined, allowing a complete analysis
of histopathological images by reporting whether the sample is normal or
malignant, and, in the last case, a heatmap of the malignant areas with their
corresponding Gleason pattern.
The studied algorithms were also used for other medical image analysis
tasks. The performance of these systems were evaluated, discussing the obtained
results, presenting conclusions and proposing improvements for future works
Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities
© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis
Ultrasound Guided Robot for Human Liver Biopsy using High Intensity Focused Ultrasound for Hemostasis
Percutaneous liver biopsy is the gold standard among clinician\u27s tool to diagnose and guide subsequent therapy for liver disease. Ultrasound image guidance is being increasingly used to reduce associated procedural risks but post–biopsy complications still persist. The major and most common complication is hemorrhage, which is highly unpredictable and may sometimes lead to death. Though the risk of mortality is low, it is too high for a diagnostic procedure. Post-biopsy care and additional surgical intervention to arrest hemorrhage make liver biopsy a costly procedure for health care delivery systems. Non-invasive methods to stop bleeding exist like electro–cautery, microwave, lasers, radio frequency, argon–beam, and High Intensity Focused Ultrasound (HIFU). All the methods except HIFU require direct exposure of the needle puncture site for hemostasis. HIFU is an ultrasound modality and uses mechanical sound waves for focused energy delivery. Ultrasound waves are minimally affected by tissue attenuation and focus internal targets without direct exposure. Human error in focusing HIFU renders it unusable for a medical procedure especially when noninvasive.
In this project we designed and developed an ultrasound guided prototype robot for accurate HIFU targeting to induce hemostasis. The robotic system performs percutaneous needle biopsy and a 7.5 cm focal length HIFU is fired at the puncture point when the needle tip retracts to the liver surface after sample collection. The robot has 4 degrees of freedom (DOF) for biopsy needle insertion, HIFU positioning, needle angle alignment and US probe image plane orientation. As the needle puncture point is always in the needle path, mechanically constraining the HIFU to focus on the needle reduced the required functionality significantly. Two mini c-arms are designed for needle angle alignment and US probe image plane orientation. This reduced the contact foot print of the robot over the patient providing a greater dexterity for positioning the robot. The robot is validated for HIFU hemostasis by a series of experiments on chicken breasts.
HIFU initiated hemorrhage control with robotic biopsy ensures arrest of post-biopsy hemorrhage and decreases patient anxiety, hospital stay, morbidity, time of procedure, and cost. This can also be extended to other organs like kidneys, lungs etc. and has widespread implications such as control of hemorrhage in post-biopsies in patients with reduced ability for hemostasis. This research opens a greater scope for research for automation and design making it a physician friendly tool for eventual clinical use
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System
Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)
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 tecnologÃas 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 microscopÃa 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 oncologÃa, donde las terapias
contra el cáncer se pueden optimizar en función de la apariencia tisular especÃfica de
cada tumor. La patologÃa 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 microscopÃa 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 caracterÃsticos. Estos tintes acentúan diferentes tipos de células y tejidos
con colores especÃficos, lo que facilita a los profesionales médicos la identificación
de caracterÃsticas 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 microscopÃa 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 clÃnico 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 histologÃa, imagen médica, matemáticas y arte,
para alinear secciones de microscopÃa 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 microscopÃa 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 microscopÃa, mejorando asà 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 morfologÃa 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 clÃnicas 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
quÃmicos 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,
tÃpico de muchas modalidades de imagen.
En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por
ordenador en el registro de microscopÃa 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 caracterÃsticas los hacen perfectos para tender
puentes robustos entre los investigadores de inteligencia artificial y los clÃnicos e
impulsar asà la patologÃa computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y TecnologÃa Biomédica por la Universidad Carlos III de MadridPresidenta: MarÃa Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo RÃos Muñoz.- Vocal: EstÃbaliz Gómez de Marisca
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