9,246 research outputs found
Automated quantification of 3D wound morphology by machine learning and optical coherence tomography in type 2 diabetes
Background: Driven by increased prevalence of type 2 diabetes and ageing populations, wounds affect millions of people each year, but monitoring and treatment remain limited. Glucocorticoid (stress hormones) activation by the enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) also impairs healing. We recently reported that 11β-HSD1 inhibition with oral AZD4017 improves acute wound healing by manual 2D optical coherence tomography (OCT), although this method is subjective and labour-intensive.
Objectives: Here, we aimed to develop an automated method of 3D OCT for rapid identification and quantification of multiple wound morphologies.
Methods: We analysed 204 3D OCT scans of 3 mm punch biopsies representing 24 480 2D wound image frames. A u-net method was used for image segmentation into 4 key wound morphologies: early granulation tissue, late granulation tissue, neo-epidermis, and blood clot. U-net training was conducted with 0.2% of available frames, with a mini-batch accuracy of 86%. The trained model was applied to compare segment area (per frame) and volume (per scan) at days 2 and 7 post-wounding and in AZD4017 compared to placebo.
Results: Automated OCT distinguished wound tissue morphologies, quantifying their volumetric transition during healing, and correlating with corresponding manual measurements. Further, AZD4017 improved epidermal re-epithelialisation (by manual OCT) with a corresponding trend towards increased neo-epidermis volume (by automated OCT).
Conclusion: Machine learning and OCT can quantify wound healing for automated, non-invasive monitoring in real-time. This sensitive and reproducible new approach offers a step-change in wound healing research, paving the way for further development in chronic wounds
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression.
For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired.
In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database
Age exacerbates the effect of myopia on retinal capillaries and string vessels
The retinal vasculature supplies oxygen and nutrition to the cells and is crucial for an adequate retinal function. In myopia, excessive eye growth is associated with various anatomical changes that can lead to myopia-related complications. However, how myopia-induced ocular growth affects the integrity of the aged retinal microvasculature at the cellular level is not well understood. Here, we studied how aging interacts with myopia-induced alteration of the retinal microvasculature in fourteen marmoset retinas (Callithrix jacchus). String vessel and capillary branchpoint were imaged and quantified in all four capillary plexi of the retinal vasculature. As marmosets with lens-induced myopia aged, they developed increasing numbers of string vessels in all four vascular plexi, with increased vessel branchpoints in the parafoveal and peripapillary retina and decreased vessel branchpoints in the peripheral retina. These myopia-induced changes to the retinal microvasculature suggest an adaptive reorganization of the retinal microvascular cellular structure template with aging and during myopia development and progression
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
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Designing carbon fibre-reinforced composites with improved structural retention on exposure to heat/fire
Carbon fibre-reinforced composites (CFRCs) are increasing in popularity due to their high
strength-to-weight ratio and resistance to corrosion. However, when exposed to temperatures
above 300°C, the polymer matrix within CFRCs decomposes and then starts burning, exposing
carbon fibres to the surroundings. The residual carbon fibres being electrically conductive, may
pose a hazard to the surrounding electronics. Moreover, at over 550°C the carbon fibres begin
to oxidise. This can lead to fibre defibrillation which also poses significant harm to human health
as broken fibres can be sharp enough to cut through human skin, and under 7µm these particles
are considered respirable where on inhalation they can causes damage to the trachea and lungs.
While considerable work has been carried out on assessing the effect of heat/fire on degradation
of the composite resin (matrix) and CFRCs themselves, there are limited studies on identifying
the damage to carbon fibres within CFRCs and the hazards posed by the exposed damaged
carbon fibres. This study examined the damage caused by high temperatures, radiant heat and
flames on carbon fibres and CFRCs, and the effects on their physical properties. A methodology
was developed to study and quantify the structural damage to carbon fibres and CFRCs after
exposure to a range of heat/fire conditions. These included thermogravimetric analysis (up to
900oC in nitrogen and air atmospheres), the tube furnace (450oC–900oC), cone calorimeter
(35kWm-2
to 75kWm-2
) and a propane burner (116kWm-2
) to simulate jet fuel fire conditions.
Residual fibres were removed from different parts of the CFRCs and the physical properties
were studied, such as fibre diameter reduction, change in electrical conductivity and decrease in
tensile strength. It was found that at heat fluxes ≥60kWm-2
oxidation of the carbon fibres
occurred. After 10min exposure to the propane flame, fibres in direct contact with the flame
showed signs of internal oxidation.The aim of this PhD project was to also improve the structural retention of CFRCs on exposure
to heat/fire so that the structural integrity is maintained and the carbon fibres are not exposed to
the environment. To address this, the following approaches were undertaken:
• Modification of the resin by adding flame retardants and nanoparticles in order to reduce the
flammability of CFRCs, improve the mechanical integrity of the char and its adherence to the fibre. Flame retardants included ammonium polyphosphate, resorcinol bis-(diphenyl
phosphate), 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide, and the nano-additives,
nano-clay, layered double hydroxide and carbon nano-tubes. Cone calorimeter testing at
75kWm-2 showed that the addition of 15wt% ammonium polyphosphate resulted in large char
formation and adherence to fibres in the underlying plies, which resulted in less oxidation to
these carbon fibres. The addition of layered double hydroxides and carbon nano-tubes on the
other hand caused pitting on fibres.
• Provide heat protection to carbon fibres within CFRCs by the inclusion of high performance
fibrous veils/woven fabrics of aramid, basalt, E-glass, polyphenylene sulphide and Kevlar.
The inclusion of the woven E-glass resulted in a notable reduction in the percentage of carbon
fibre oxidised. However, the volatiles produced during the decomposition of Kevlar and PPS
sensitised the carbon fibre to oxidation, causing it to occur more rapidly and at a lower
temperature.
• Using high temperature chemical coatings to individually coat carbon fibres prior to making
the CFRCs. Ceramic compounds (silica, alumina and zirconia), chosen as coating materials
because of their high thermal stability, were applied by different processes. The most
promising coatings included alumina and silica formed via sol-gel process and polysiloxane
deposited during plasma exposure. Tows coated in these chemicals underwent heat testing in
a tube furnace where those coated with alumina maintained the largest fibre diameters. While
polysiloxane coating provided oxidation protection up to 600°C, after which cracks in the
coating were observed. This was attributed to the mechanical mismatch of the polysiloxane
coating and the carbon fibre
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