2,510 research outputs found
Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections
Background Prostate cancer is one of the leading causes of cancer related deaths. For diagnosis, predicting the outcome of the disease, and for assessing potential new biomarkers, pathologists and researchers routinely analyze histological samples. Morphological and molecular information may be integrated by aligning microscopic histological images in a multiplex fashion. This process is usually time-consuming and results in intra- and inter-user variability. The aim of this study is to investigate the feasibility of using modern image analysis methods for automated alignment of microscopic images from differently stained adjacent paraffin sections from prostatic tissue specimens. Methods Tissue samples, obtained from biopsy or radical prostatectomy, were sectioned and stained with either hematoxylin & eosin (H&E), immunohistochemistry for p63 and AMACR or Time Resolved Fluorescence (TRF) for androgen receptor (AR). Image pairs were aligned allowing for translation, rotation and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale invariant image transform (SIFT), followed by the well-known RANSAC protocol for finding point correspondences and finally aligned by Procrustes fit. The Registration results were evaluated using both visual and quantitative criteria as defined in the text. Results Three experiments were carried out. First, images of consecutive tissue sections stained with H&E and p63/AMACR were successfully aligned in 85 of 88 cases (96.6%). The failures occurred in 3 out of 13 cores with highly aggressive cancer (Gleason score ≥ 8). Second, TRF and H&E image pairs were aligned correctly in 103 out of 106 cases (97%). The third experiment considered the alignment of image pairs with the same staining (H&E) coming from a stack of 4 sections. The success rate for alignment dropped from 93.8% in adjacent sections to 22% for sections furthest away. Conclusions The proposed method is both reliable and fast and therefore well suited for automatic segmentation and analysis of specific areas of interest, combining morphological information with protein expression data from three consecutive tissue sections. Finally, the performance of the algorithm seems to be largely unaffected by the Gleason grade of the prostate tissue samples examined, at least up to Gleason score 7
Differently stained whole slide image registration technique with landmark validation
Abstract. One of the most significant features in digital pathology is to compare and fuse successive differently stained tissue sections, also called slides, visually. Doing so, aligning different images to a common frame, ground truth, is required. Current sample scanning tools enable to create images full of informative layers of digitalized tissues, stored with a high resolution into whole slide images. However, there are a limited amount of automatic alignment tools handling large images precisely in acceptable processing time. The idea of this study is to propose a deep learning solution for histopathology image registration. The main focus is on the understanding of landmark validation and the impact of stain augmentation on differently stained histopathology images. Also, the developed registration method is compared with the state-of-the-art algorithms which utilize whole slide images in the field of digital pathology.
There are previous studies about histopathology, digital pathology, whole slide imaging and image registration, color staining, data augmentation, and deep learning that are referenced in this study. The goal is to develop a learning-based registration framework specifically for high-resolution histopathology image registration. Different whole slide tissue sample images are used with a resolution of up to 40x magnification. The images are organized into sets of consecutive, differently dyed sections, and the aim is to register the images based on only the visible tissue and ignore the background. Significant structures in the tissue are marked with landmarks.
The quality measurements include, for example, the relative target registration error, structural similarity index metric, visual evaluation, landmark-based evaluation, matching points, and image details. These results are comparable and can be used also in the future research and in development of new tools. Moreover, the results are expected to show how the theory and practice are combined in whole slide image registration challenges. DeepHistReg algorithm will be studied to better understand the development of stain color feature augmentation-based image registration tool of this study. Matlab and Aperio ImageScope are the tools to annotate and validate the image, and Python is used to develop the algorithm of this new registration tool.
As cancer is globally a serious disease regardless of age or lifestyle, it is important to find ways to develop the systems experts can use while working with patients’ data. There is still a lot to improve in the field of digital pathology and this study is one step toward it.Eri menetelmin värjättyjen virtuaalinäytelasien rekisteröintitekniikka kiintopisteiden validointia hyödyntäen. Tiivistelmä. Yksi tärkeimmistä digitaalipatologian ominaisuuksista on verrata ja fuusioida peräkkäisiä eri menetelmin värjättyjä kudosleikkeitä toisiinsa visuaalisesti. Tällöin keskenään lähes identtiset kuvat kohdistetaan samaan yhteiseen kehykseen, niin sanottuun pohjatotuuteen. Nykyiset näytteiden skannaustyökalut mahdollistavat sellaisten kuvien luonnin, jotka ovat täynnä kerroksittaista tietoa digitalisoiduista näytteistä, tallennettuna erittäin korkean resoluution virtuaalisiin näytelaseihin. Tällä hetkellä on olemassa kuitenkin vain kourallinen automaattisia työkaluja, jotka kykenevät käsittelemään näin valtavia kuvatiedostoja tarkasti hyväksytyin aikarajoin. Tämän työn tarkoituksena on syväoppimista hyväksikäyttäen löytää ratkaisu histopatologisten kuvien rekisteröintiin. Tärkeimpänä osa-alueena on ymmärtää kiintopisteiden validoinnin periaatteet sekä eri väriaineiden augmentoinnin vaikutus. Lisäksi tässä työssä kehitettyä rekisteröintialgoritmia tullaan vertailemaan muihin kirjallisuudessa esitettyihin algoritmeihin, jotka myös hyödyntävät virtuaalinäytelaseja digitaalipatologian saralla.
Kirjallisessa osiossa tullaan siteeraamaan aiempia tutkimuksia muun muassa seuraavista aihealueista: histopatologia, digitaalipatologia, virtuaalinäytelasi, kuvantaminen ja rekisteröinti, näytteen värjäys, data-augmentointi sekä syväoppiminen. Tavoitteena on kehittää oppimispohjainen rekisteröintikehys erityisesti korkearesoluutioisille digitalisoiduille histopatologisille kuville. Erilaisissa näytekuvissa tullaan käyttämään jopa 40-kertaista suurennosta. Kuvat kudoksista on järjestetty eri menetelmin värjättyihin peräkkäisiin kuvasarjoihin ja tämän työn päämääränä on rekisteröidä kuvat pohjautuen ainoastaan kudosten näkyviin osuuksiin, jättäen kuvien tausta huomioimatta. Kudosten merkittävimmät rakenteet on merkattu niin sanotuin kiintopistein.
Työn laatumittauksina käytetään arvoja, kuten kohteen suhteellinen rekisteröintivirhe (rTRE), rakenteellisen samankaltaisuuindeksin mittari (SSIM), sekä visuaalista arviointia, kiintopisteisiin pohjautuvaa arviointia, yhteensopivuuskohtia, ja kuvatiedoston yksityiskohtia. Nämä arvot ovat verrattavissa myös tulevissa tutkimuksissa ja samaisia arvoja voidaan käyttää uusia työkaluja kehiteltäessä. DeepHistReg metodi toimii pohjana tässä työssä kehitettävälle näytteen värjäyksen parantamiseen pohjautuvalle rekisteröintityökalulle. Matlab ja Aperio ImageScope ovat ohjelmistoja, joita tullaan hyödyntämään tässä työssä kuvien merkitsemiseen ja validointiin. Ohjelmointikielenä käytetään Pythonia.
Syöpä on maailmanlaajuisesti vakava sairaus, joka ei katso ikää eikä elämäntyyliä. Siksi on tärkeää löytää uusia keinoja kehittää työkaluja, joita asiantuntijat voivat hyödyntää jokapäiväisessä työssään potilastietojen käsittelyssä. Digitaalipatologian osa-alueella on vielä paljon innovoitavaa ja tämä työ on yksi askel eteenpäin taistelussa syöpäsairauksia vastaan
Potential use of oxygen as a metabolic biosensor in combination with T2*-weighted MRI to define the ischemic penumbra
We describe a novel magnetic resonance imaging technique for detecting metabolism indirectly through changes in oxyhemoglobin:deoxyhemoglobin ratios and T2* signal change during ‘oxygen challenge’ (OC, 5 mins 100% O2). During OC, T2* increase reflects O2 binding to deoxyhemoglobin, which is formed when metabolizing tissues take up oxygen. Here OC has been applied to identify tissue metabolism within the ischemic brain. Permanent middle cerebral artery occlusion was induced in rats. In series 1 scanning (n=5), diffusion-weighted imaging (DWI) was performed, followed by echo-planar T2* acquired during OC and perfusion-weighted imaging (PWI, arterial spin labeling). Oxygen challenge induced a T2* signal increase of 1.8%, 3.7%, and 0.24% in the contralateral cortex, ipsilateral cortex within the PWI/DWI mismatch zone, and ischemic core, respectively. T2* and apparent diffusion coefficient (ADC) map coregistration revealed that the T2* signal increase extended into the ADC lesion (3.4%). In series 2 (n=5), FLASH T2* and ADC maps coregistered with histology revealed a T2* signal increase of 4.9% in the histologically defined border zone (55% normal neuronal morphology, located within the ADC lesion boundary) compared with a 0.7% increase in the cortical ischemic core (92% neuronal ischemic cell change, core ADC lesion). Oxygen challenge has potential clinical utility and, by distinguishing metabolically active and inactive tissues within hypoperfused regions, could provide a more precise assessment of penumbra
Patch-based nonlinear image registration for gigapixel whole slide images
Producción CientíficaImage registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Histopathological cancer diagnosis is based on visual examination of stained
tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely
employed worldwide. It is easy to acquire and cost effective, but cells and
tissue components show low-contrast with varying tones of dark blue and pink,
which makes difficult visual assessments, digital image analysis, and
quantifications. These limitations can be overcome by IHC staining of target
proteins of the tissue slide. IHC provides a selective, high-contrast imaging
of cells and tissue components, but their use is largely limited by a
significantly more complex laboratory processing and high cost. We proposed a
conditional CycleGAN (cCGAN) network to transform the H\&E stained images into
IHC stained images, facilitating virtual IHC staining on the same slide. This
data-driven method requires only a limited amount of labelled data but will
generate pixel level segmentation results. The proposed cCGAN model improves
the original network \cite{zhu_unpaired_2017} by adding category conditions and
introducing two structural loss functions, which realize a multi-subdomain
translation and improve the translation accuracy as well. % need to give
reasons here. Experiments demonstrate that the proposed model outperforms the
original method in unpaired image translation with multi-subdomains. We also
explore the potential of unpaired images to image translation method applied on
other histology images related tasks with different staining techniques
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
Registration of serial sections: An evaluation method based on distortions of the ground truths
Registration of histological serial sections is a challenging task. Serial
sections exhibit distortions and damage from sectioning. Missing information on
how the tissue looked before cutting makes a realistic validation of 2D
registrations extremely difficult.
This work proposes methods for ground-truth-based evaluation of
registrations. Firstly, we present a methodology to generate test data for
registrations. We distort an innately registered image stack in the manner
similar to the cutting distortion of serial sections. Test cases are generated
from existing 3D data sets, thus the ground truth is known. Secondly, our test
case generation premises evaluation of the registrations with known ground
truths. Our methodology for such an evaluation technique distinguishes this
work from other approaches. Both under- and over-registration become evident in
our evaluations. We also survey existing validation efforts.
We present a full-series evaluation across six different registration methods
applied to our distorted 3D data sets of animal lungs. Our distorted and ground
truth data sets are made publicly available.Comment: Supplemental data available under https://zenodo.org/record/428244
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