2,742 research outputs found
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Development and validation of novel and quantitative MRI methods for cancer evaluation
Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic.
To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field.
The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)
Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability
Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far.
In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs.
We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes.
We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
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
Image and Video Processing Methods for Studying hiPSC-derived Cardiomyocyte Biomechanics
Cardiovascular disease (CVD) is the most common cause of death globally. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a suitable model for studying CVD and developing new treatments. Using hiPSC-CMs in heart disease modelling has several benefits compared to using other cell types such as embryonic stem cells or animal cells. Many cardiovascular diseases are known to affect cardiomyocyte behaviour including their biomechanics. Therefore, studying the contractile machinery and force production of hiPSC-CMs is central in CVD research.
Microscopy imaging methods are required for scientists to view cells, their contractile structures and study their force production. Namely fluorescence microscopy and traction force microscopy -based imaging methods are often used in studying cellular biomechanics. To quantify and extract information from microscopy images and videos, image and video processing methods are required. Therefore, image and video processing methods are a central part of studying hiPSC-CM biomechanics and CVD.
The ever-increasing amount of imaging data requires high-throughput, fast, accurate and automated image, and video processing methods. The aim of this thesis is to provide a review of the state-of-the-art image and video processing methods and tools that can be used for studying hiPSC-CM biomechanics. The focus of this thesis is on machine learning -based and fully automated image and video processing methods. Moreover, this thesis acts as an introductory guide of the available methods and tools for researchers interested in image and video processing for studying hiPSC-CM biomechanics.
This thesis is divided into three main sections. In the first section the working principles of fluorescence microscopy and traction force microscopy are introduced in addition to their applications in studying hiPSC-CM biomechanics. In the second and third sections image and video processing methods and tools for fluorescence microscopy and traction force microscopy are introduced respectively.Sydän- ja verisuonisairaudet ovat maailmanlaajuisesti merkittävin kuolinsyy. Ihmisen indusoiduista pluripotenteista kantasolu-johteisista soluista (engl. human induced pluripotent stem cell, hiPSC) voidaan erilaistaa sydänlihassoluja. hiPSC-johteiset sydänlihassolut (engl. hiPSC-derived cardiomyocyte, hiPSC-CM) ovat osoittautuneet hyödyllisiksi sydänsairauksien mallinnuksessa. Niiden käyttöön liittyy useita etuja verrattuna esimerkiksi alkion kantasoluihin tai eläinperäisiin sydänlihassoluhin. Sydänsairaudet vaikuttavat sydänlihassolujen käyttäytymiseen ja niiden biomekaniikkaan. Tämän vuoksi sydänlihassolujen voimaatuottavien rakenteiden ja voiman tuottamisen tutkiminen on keskeistä sydänsairauksien mallinnuksessa ja uusien hoitojen kehityksessä.
Jotta soluja, niiden voimaa tuottavia rakenteita ja voimantuottoa voidaan tutkia, tarvitaan mikroskooppikuvantamista. Erityisesti fluoresenssimikroskopia ja siihen perustuvat muut kuvantamismenetelmät kuten traktiomikroskopia (engl. traction force microscopy, TFM) ovat osoittautuneet hyödyllisiksi hiPSC-CM-solujen biomekaniikan tutkimuksessa. Kuvista tai videoista saadun informaation kvantifioimiseksi puolestaan tarvitaan erilaisia kuvan- ja videonkäsittelyn menetelmiä. Tämän vuoksi kuvan- ja videonkäsittelyn menetelmät ovat keskeinen osa hiPSC-CM-solujen biomekaniikan ja sydänsairauksien tutkimusta.
Jatkuvasti kasvava kuvantamisdatan määrä vaatii suurikapasiteettisia, nopeita, tarkkoja ja automatisoituja kuvan- ja videonkäsittelyn menetelmiä. Siksi tämän kirjallisuuskatsauksen tavoitteena on esitellä sekä huippuluokan että uusimpia kuvan- ja videonkäsittelyn menetelmiä hiPSC-CM-solujen biomekaniikan tutkimukseen keskittyen automatisoituihin ja koneoppimiseen perustuviin menetelmiin. Lisäksi tämä työ toimii oppaana saatavilla olevista kuvan- ja videonkäsittelyn menetelmistä sekä työkaluista hiPSC-CM-solujen biomekaniikan tutkimuksesta kiinnostuneille tutkijoille.
Tämä työ jakautuu kolmeen pääosaan. Ensin lukijalle esitellään työn taustaa hiPSC-CM-soluista sekä kahdesta mikroskooppikuvantamismenetelmästä. Ensin käsitellään fluoresenssimikroskopian ja sitten traktiomikroskopian toimintaperiaatteita ja sovelluksia hiPSC-CM-solujen biomekaniikan tutkimuksessa. Seuraavassa osiossa perehdytään fluoresenssimikroskopialle soveltuviin kuvan- ja videonkäsittelyn menetelmiin ja työkaluihin keskittyen esikäsittelyyn, segmentointiin ja kvantitatiiviseen analyysiin. Kolmannessa osiossa esitellään traktiomikroskopialle soveltuvia kuvan- ja videonkäsittelyn menetelmiä ja työkaluja keskittyen traktiovoimien määritykseen
- …