Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Nephroblastoma analysis in MRI images

The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is needed to facilitate the evaluation and the treatments of the tumour. The proposed method serves this purpose by performing the segmentation of nephroblastoma in MRI scans. The segmentation is performed by adapting and a 2D free hand drawing tool to select a region of interest in the scan slices. Results from 24 patients show a mean root-mean-square error of 0.0481±0.0309, an average Dice coefficient of 0.9060±0.0549 and an average accuracy of 99.59% ±0.0039. Thus the proposed method demonstrated an effective agreement with manual annotations

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

Abstract Whilst urban areas are thriving in trialling new mobility services (NMS), rural environments, often perceived as areas of low demand for travel, struggle to attract investments for creating more mobility solutions alongside traditional public transport (PT) services, making residents more reliant on private cars. This paper describes how policy interventions for introducing NMS in rural areas should be guided by big data to capture real and accurate travel behaviours, therefore avoiding perceived biases and potentially underestimating demand. In the UK, the provision of transport in rural areas is solely linked to population density and does not consider differences between places and residents’ travel habits. The proposed data‐driven decision‐making process used trip‐chains from mobile network data (MND) to derive recent and accurate travel patterns from residents and provide the right mix of on‐demand mobility services alongside existing fixed scheduled public transport (PT). The manuscript describes the steps carried out to study three rural areas at low, medium and high population density in the UK: a data landscape to select study areas; the development of an activity‐based model, which uses anonymised mobile network data (MND) aggregated at trip‐chains level to derive travel patterns; and the development of an on‐line questionnaire and focus groups with rural communities to co‐designing solutions based on attitudes towards NMS. Results demonstrated that a data‐driven decision making process to introduce NMS is a viable solution for updating demand for travel in rural areas, offering a broad understanding of mobility needs and the relationship of interdependency with nearby areas, therefore allowing policy makers to create users‐centric transport solutions. The study concludes by drawing recommendations for NMS for passengers and goods for the NMS proposed for a rural areas [Demand Responsive Transport (DRT), Micro‐mobility and delivery drones]

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

Peer reviewed: TruePublication status: PublishedThe massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.</jats:p

Dynamics of Variants of Concern (VOC) of SARS-CoV-2 during the Different Waves of COVID-19 in Senegal

Background: In Senegal, the incidence of SARS-CoV-2 evolved with four successive epidemic waves. The first wave started in March 2020 with low virus variability, whilst the second outbreak, which started in December 2020, was dominated by the Alpha variant. The third wave took place in June 2021, and the fourth at the end of November 2021. Our interest was to investigate the involvement of variants of concern during these four waves and to track the viral diversity of SARS-CoV-2. Methodology: During the four waves of the pandemic, 276,876 nasopharyngeal swabs were analyzed at the Institut de Recherche en Sant&eacute;, de Surveillance Epid&eacute;miologique et de Formation (IRESSEF). Of these, 22,558 samples tested positive for SARS-CoV-2 by RT-PCR. Then, the virus genomes were sequenced in 817 positive samples using the ARTIC Network of Oxford Nanopore Technologies (ONT). In addition, 10% of the negative samples in RT-PCR new variants were also targeted for the detection of new and previously undescribed variants. Results: Our data have overall shown that the Senegalese strains are very similar to each other or closely related to other strains, such as Gambia, France etc. During the first wave, the most common clade found was 19A (67.5%) and a majority of the samples were of the B.1 (50%) lineage. We noted more diversity during the second wave where clade 20A (38.4%) was more frequent, followed by clade 20B (31.52%) and 20I (9.74%). At the level of lineages, we identified variants of concern as B.1.1.7 (9.74%) and B.1.617.2 (0.86%). In the third wave, we observed at the clade level with mainly 21A (32.63%) and 21J (16.84%). During the fourth wave at the end of November 2021, we mainly identified clade 21K Omicron variant 21K (B.1.1.529 and BA.1) (80.47%) and Delta variant (21A, 21J, and 21I) (AY.103, AY.122, AY.122.1, AY.26, AY.34, AY.36, AY.4, AY.48, AY.57, AY.61, and AY.87) (14.06%). Impact: SARS-CoV-2 diversity may affect the virus&rsquo;s properties, such as how it spreads, disease severity, or the performance of vaccines, tools, or other public health and social measures. Therefore, such tracking of SARS-CoV-2 variants is not only of public interest, but also highlights the role some African institutes such as IRESSEF with surveillance capabilities through the real-time sequencing of SARS-CoV-2 genomes in the local context. Conclusion: In Senegal, the SARS-CoV-2 pandemic has disrupted the organization of the health system. IRESSEF contributed to put in place strategies to respond effectively to the expectations of medical authorities by providing them with data on the strains circulating in Senegal at each moment of the epidemic