300 research outputs found
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research
Recommended from our members
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research
Human-Centered Content-Based Image Retrieval
Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
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
Hematological image analysis for acute lymphoblastic leukemia detection and classification
Microscopic analysis of peripheral blood smear is a critical step in detection of leukemia.However, this type of light microscopic assessment is time consuming, inherently subjective, and is governed by hematopathologists clinical acumen and experience. To
circumvent such problems, an efficient computer aided methodology for quantitative analysis of peripheral blood samples is required to be developed. In this thesis, efforts are therefore made to devise methodologies for automated detection and subclassification of Acute Lymphoblastic Leukemia (ALL) using image processing and machine learning methods.Choice of appropriate segmentation scheme plays a vital role in the automated disease recognition process. Accordingly to segment the normal mature lymphocyte and malignant lymphoblast images into constituent morphological regions novel schemes have been proposed. In order to make the proposed schemes viable from a practical and real–time stand point, the segmentation problem is addressed in both supervised and unsupervised framework. These proposed methods are based on neural network,feature space clustering, and Markov random field modeling, where the segmentation problem is formulated as pixel classification, pixel clustering, and pixel labeling
problem respectively. A comprehensive validation analysis is presented to evaluate the performance of four proposed lymphocyte image segmentation schemes against manual
segmentation results provided by a panel of hematopathologists. It is observed that morphological components of normal and malignant lymphocytes differ significantly. To automatically recognize lymphoblasts and detect ALL in peripheral blood samples, an efficient methodology is proposed.Morphological, textural and color features are extracted from the segmented nucleus and cytoplasm regions of the lymphocyte images. An ensemble of classifiers represented as EOC3 comprising of three classifiers shows highest classification accuracy of 94.73% in comparison to individual members. The subclassification of ALL based on French–American–British (FAB) and World
Health Organization (WHO) criteria is essential for prognosis and treatment planning. Accordingly two independent methodologies are proposed for automated classification of malignant lymphocyte (lymphoblast) images based on morphology and phenotype. These methods include lymphoblast image segmentation, nucleus and cytoplasm feature extraction, and efficient classification
Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.
Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.
Background: Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage.Methods: A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm's robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods.Results: Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage.Conclusions: Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues
- …