1,645 research outputs found
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences
The measurement of retinal blood flow (RBF) in capillaries can provide a
powerful biomarker for the early diagnosis and treatment of ocular diseases.
However, no single modality can determine capillary flowrates with high
precision. Combining erythrocyte-mediated angiography (EMA) with optical
coherence tomography angiography (OCTA) has the potential to achieve this goal,
as EMA can measure the absolute 2D RBF of retinal microvasculature and OCTA can
provide the 3D structural images of capillaries. However, multimodal retinal
image registration between these two modalities remains largely unexplored. To
fill this gap, we establish MEMO, the first public multimodal EMA and OCTA
retinal image dataset. A unique challenge in multimodal retinal image
registration between these modalities is the relatively large difference in
vessel density (VD). To address this challenge, we propose a segmentation-based
deep-learning framework (VDD-Reg) and a new evaluation metric (MSD), which
provide robust results despite differences in vessel density. VDD-Reg consists
of a vessel segmentation module and a registration module. To train the vessel
segmentation module, we further designed a two-stage semi-supervised learning
framework (LVD-Seg) combining supervised and unsupervised losses. We
demonstrate that VDD-Reg outperforms baseline methods quantitatively and
qualitatively for cases of both small VD differences (using the CF-FA dataset)
and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires
as few as three annotated vessel segmentation masks to maintain its accuracy,
demonstrating its feasibility.Comment: Submitted to IEEE JBH
Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
Medical imaging plays a prominent role in modern clinical practice for numerous
medical specialties. For instance, in ophthalmology, different imaging techniques are
commonly used to visualize and study the eye fundus. In this context, automated
image analysis methods are key towards facilitating the early diagnosis and adequate
treatment of several diseases. Nowadays, deep learning algorithms have already
demonstrated a remarkable performance for different image analysis tasks. However,
these approaches typically require large amounts of annotated data for the training
of deep neural networks. This complicates the adoption of deep learning approaches,
especially in areas where large scale annotated datasets are harder to obtain, such
as in medical imaging.
This thesis aims to explore novel approaches for the automated analysis of medical
images, particularly in ophthalmology. In this regard, the main focus is on
the development of novel deep learning-based approaches that do not require large
amounts of annotated training data and can be applied to high resolution images.
For that purpose, we have presented a novel paradigm that allows to take advantage
of unlabeled complementary image modalities for the training of deep neural
networks. Additionally, we have also developed novel approaches for the detailed
analysis of eye fundus images. In that regard, this thesis explores the analysis of
relevant retinal structures as well as the diagnosis of different retinal diseases. In
general, the developed algorithms provide satisfactory results for the analysis of the
eye fundus, even when limited annotated training data is available.[Resumen]
Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna
de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso
de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este
contexto, los métodos automáticos de análisis de imagen son clave para facilitar
el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la
actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable
rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos
suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de
las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje
profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados
son más difíciles de obtener, como es el caso de la imagen médica.
Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco
principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no
requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan
aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo
paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas
para el entrenamiento de redes neuronales profundas. Además, también
hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del
fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas
relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En
general, los algoritmos desarrollados proporcionan resultados satisfactorios para el
análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos
de entrenamiento etiquetados es limitada.[Resumo]
As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de
numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso
de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste
contexto, os métodos automáticos de análises de imaxe son clave para facilitar o
diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade,
os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento
en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar
grandes cantidades de datos etiquetos para o adestramento das redes neuronais
profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente
en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles
de obter, como é o caso da imaxe médica.
Esta tese ten como obxectivo explorar novos métodos para a análise automática
de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal
é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non
requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse
a imaxes de alta resolución. Para iso, presentamos un novo paradigma que
permite aproveitar modalidades de imaxe complementarias non etiquetadas para o
adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos
métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta
tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de
diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan
resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo
cando a dispoñibilidade de datos de adestramento etiquetados é limitada
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective
The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care
Medical image registration using unsupervised deep neural network: A scoping literature review
In medicine, image registration is vital in image-guided interventions and
other clinical applications. However, it is a difficult subject to be addressed
which by the advent of machine learning, there have been considerable progress
in algorithmic performance has recently been achieved for medical image
registration in this area. The implementation of deep neural networks provides
an opportunity for some medical applications such as conducting image
registration in less time with high accuracy, playing a key role in countering
tumors during the operation. The current study presents a comprehensive scoping
review on the state-of-the-art literature of medical image registration studies
based on unsupervised deep neural networks is conducted, encompassing all the
related studies published in this field to this date. Here, we have tried to
summarize the latest developments and applications of unsupervised deep
learning-based registration methods in the medical field. Fundamental and main
concepts, techniques, statistical analysis from different viewpoints,
novelties, and future directions are elaborately discussed and conveyed in the
current comprehensive scoping review. Besides, this review hopes to help those
active readers, who are riveted by this field, achieve deep insight into this
exciting field
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