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1. British societies guideline on the management of emergencies in implantable left ventricular assist device recipients in transplant centres

Author

Akhtar, Waqas, Baston, Veronica Rial, Berman, Marius, Bhagra, Sai, Chue, Colin, Deakin, Charles D., Dalzell, Jonathan R., Dunning, Joel, Dunning, John, Gardner, Roy S., Kiff, Kristine, Kore, Shishir, Lim, Sern, MacGowan, Guy, Naldrett, Ian, Ostermann, Marlies, Pinto, Sofia, Pettit, Stephen, Gil, Fernando Riesgo, Rosenberg, Alex, Rubino, Antonio, Sayeed, Rana, Sequeira, Joao, Swanson, Neil, Tsui, Steven, Walker, Christopher, Webb, Stephen, Woods, Andrew, Ventkateswaran, Rajamiyer, and Bowles, Christopher T.

Published
2024
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3. 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

Author

Attar, Rahman, Pereanez, Marco, Bowles, Christopher, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance., Comment: Accepted for publication in MICCAI 2019

Published
2019

4. GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation

Author

Bowles, Christopher, Gunn, Roger, Hammers, Alexander, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical imaging is a domain which suffers from a paucity of manually annotated data for the training of learning algorithms. Manually delineating pathological regions at a pixel level is a time consuming process, especially in 3D images, and often requires the time of a trained expert. As a result, supervised machine learning solutions must make do with small amounts of labelled data, despite there often being additional unlabelled data available. Whilst of less value than labelled images, these unlabelled images can contain potentially useful information. In this paper we propose combining both labelled and unlabelled data within a GAN framework, before using the resulting network to produce images for use when training a segmentation network. We explore the task of deep grey matter multi-class segmentation in an AD dataset and show that the proposed method leads to a significant improvement in segmentation results, particularly in cases where the amount of labelled data is restricted. We show that this improvement is largely driven by a greater ability to segment the structures known to be the most affected by AD, thereby demonstrating the benefits of exposing the system to more examples of pathological anatomical variation. We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.

Published
2018

5. GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

Author

Bowles, Christopher, Chen, Liang, Guerrero, Ricardo, Bentley, Paul, Gunn, Roger, Hammers, Alexander, Dickie, David Alexander, Hernández, Maria Valdés, Wardlaw, Joanna, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available.

Published
2018

6. Medical image synthesis in the diagnosis and study of neurodegenerative diseases

Author

Bowles, Christopher and Reuckert, Daniel

Subjects
004
Abstract

Medical imaging is a cornerstone of modern healthcare. The ability to acquire images from inside a patient has revolutionised the way doctors diagnose and treat diseases, with almost all clinical pipelines now involving imaging to some degree. The development of these imaging methods has led to the field of medical image computing, where a multitude of tools and techniques have been proposed to aid clinicians and researchers in interpreting and analysing these images. One such family of techniques involves generating synthetic medical images. Image synthesis techniques are wide and varied, ranging from basic phantoms, to disease atlases, to high resolution photo-realistic subject-specific images. Their applications are similarly diverse: for developing novel acquisition protocols, training and testing algorithms, visualising changes in disease, predicting particular image types from others, and improving image quality and resolution. This thesis examines the use of medical image synthesis with a particular focus on applications in neurodegenerative diseases. A method for synthesising subject-specific non-pathological images from pathological images is first proposed and used for the unsupervised brain lesion segmentation. We next show how generative adversarial networks (GANs) can be used to both analyse the structural changes seen in patients with Alzheimer's disease, and to add or remove these changes from patient images to produce a subject-specific prediction of disease progression. Finally we investigate how GAN-derived synthetic data can be used to increase the size of training datasets, and under what conditions this additional data can lead to an improvement across a variety of segmentation tasks. Within this context we explore two situations: where only a small amount of labelled data is available, and where a large amount of unlabelled data is also available. We show that the proposed methods can lead to significant improvements in segmentation results, especially when a small amount of labelled data is available.

Published
2019
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7. Association of Renin‐Angiotensin‐Aldosterone System Inhibitors With Clinical Outcomes, Hemodynamics, and Myocardial Remodeling Among Patients With Advanced Heart Failure on Left Ventricular Assist Device Support

Author

Gallone, Guglielmo, primary, Ibero, Javier, additional, Morley‐Smith, Andrew, additional, Monteagudo Vela, Maria, additional, Fiorelli, Francesca, additional, Konicoff, Mailen, additional, Edwards, Gemma, additional, Raj, Binu, additional, Shanmuganathan, Mayooran, additional, Pidello, Stefano, additional, Frea, Simone, additional, De Ferrari, Gaetano Maria, additional, Panoulas, Vasileios, additional, Stock, Ulrich, additional, Bowles, Christopher, additional, Dunning, John, additional, and Riesgo Gil, Fernando, additional

Published
2024
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8. Mechanical life support algorithm for emergency management of patient receiving extracorporeal membrane oxygenation.

Author

Akhtar, Waqas, Pinto, Sofia, Gerlando, Emanuele, Pitt, Timothy, Banya, Winston, Dunning, John, Bowles, Christopher T, and Rosenberg, Alex

Subjects
EXTRACORPOREAL membrane oxygenation, DATA analysis, TERTIARY care, MANN Whitney U Test, DESCRIPTIVE statistics, SIMULATION methods in education, MEDICAL emergencies, BLOOD circulation, STATISTICS, CARDIAC arrest, CARDIOPULMONARY resuscitation, DATA analysis software, ALGORITHMS, HEALTH care teams, ADVANCED cardiac life support
Abstract

Background: There are limited practical advanced life support algorithms to aid teams in the management of cardiac arrest in patients on extracorporeal membrane oxygenation (ECMO). Methods: In our specialist tertiary referral centre we developed, by iteration, a novel resuscitation algorithm for ECMO emergencies which we validated through simulation and assessment of our multi-disciplinary team. A Mechanical Life Support course was established to provide theoretical and practical education combined with simulation to consolidate knowledge and confidence in algorithm use. We assessed these measures using confidence scoring, a key performance indicator (the time taken to resolve gas line disconnection) and a multiple choice question (MCQ) examination. Results: Following this intervention the median confidence scores increased from 2 (Interquartile range IQR 2, 3) to 4 (IQR 4, 4) out of maximum 5 (n = 53, p < 0.0001). Theoretical knowledge assessed by median MCQ score increased from 8 (6, 9) to 9 (7, 10) out of maximum 11 (n = 53, p0.0001). The use of the ECMO algorithm reduced the time taken by teams in a simulated emergency to identify a gas line disconnection and resolve the problem from median 128 s (65, 180) to 44 s (31, 59) (n = 36, p 0.001) and by a mean of 81.5 s (CI 34, 116, p = 0.001). Conclusions: We present an evidence based practical ECMO resuscitation algorithm that provides guidance to clinical teams responding to cardiac arrest in ECMO patients covering both patient and ECMO troubleshooting. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Recommendations for laboratory workflow that better support centralised amalgamation of genomic variant data: findings from CanVIG-UK national molecular laboratory survey.

Author

Allen, Sophie, Loong, Lucy, Garrett, Alice, Torr, Bethany, Durkie, Miranda, Drummond, James, Callaway, Alison, Robinson, Rachel, Burghel, George J., Hanson, Helen, Field, Joanne, McDevitt, Trudi, McVeigh, Terri P., Bedenham, Tina, Bowles, Christopher, Bradshaw, Kirsty, Brooks, Claire, Butler, Samantha, Del Rey Jimenez, Juan Carlos, and Hawkes, Lorraine

Abstract

Background National and international amalgamation of genomic data offers opportunity for research and audit, including analyses enabling improved classification of variants of uncertain significance. Review of individual-level data from National Health Service (NHS) testing of cancer susceptibility genes (2002-2023) submitted to the National Disease Registration Service revealed heterogeneity across participating laboratories regarding (1) the structure, quality and completeness of submitted data, and (2) the ease with which that data could be assembled locally for submission. Methods In May 2023, we undertook a closed online survey of 51 clinical scientists who provided consensus responses representing all 17 of 17 NHS molecular genetic laboratories in England and Wales which undertake NHS diagnostic analyses of cancer susceptibility genes. The survey included 18 questions relating to 'next-generation sequencing workflow' (11), 'variant classification' (3) and 'phenotypical context' (4). Results Widely differing processes were reported for transfer of variant data into their local LIMS (Laboratory Information Management System), for the formatting in which the variants are stored in the LIMS and which classes of variants are retained in the local LIMS. Differing local provisions and workflow for variant classifications were also reported, including the resources provided and the mechanisms by which classifications are stored. Conclusion The survey responses illustrate heterogeneous laboratory workflow for preparation of genomic variant data from local LIMS for centralised submission. Workflow is often labour-intensive and inefficient, involving multiple manual steps which introduce opportunities for error. These survey findings and adoption of the concomitant recommendations may support improvement in laboratory dataflows, better facilitating submission of data for central amalgamation. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Author

Zhang, Le, Pereañez, Marco, Bowles, Christopher, Piechnik, Stefan, Neubauer, Stefan, Petersen, Steffen, Frangi, Alejandro, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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11. Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks

Author

Zhang, Le, Pereañez, Marco, Bowles, Christopher, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Frangi, Alejandro F., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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12. Recommendations for laboratory workflow that better support centralised amalgamation of genomic variant data: findings from CanVIG-UK national molecular laboratory survey

Author

Allen, Sophie, primary, Loong, Lucy, additional, Garrett, Alice, additional, Torr, Bethany, additional, Durkie, Miranda, additional, Drummond, James, additional, Callaway, Alison, additional, Robinson, Rachel, additional, Burghel, George J, additional, Hanson, Helen, additional, Field, Joanne, additional, McDevitt, Trudi, additional, McVeigh, Terri P, additional, Bedenham, Tina, additional, Bowles, Christopher, additional, Bradshaw, Kirsty, additional, Brooks, Claire, additional, Butler, Samantha, additional, Del Rey Jimenez, Juan Carlos, additional, Hawkes, Lorraine, additional, Stinton, Victoria, additional, MacMahon, Suzanne, additional, Owens, Martina, additional, Palmer-Smith, Sheila, additional, Smith, Kenneth, additional, Tellez, James, additional, Valganon-Petrizan, Mikel, additional, Waskiewicz, Erik, additional, Yau, Michael, additional, Eccles, Diana M, additional, Tischkowitz, Marc, additional, Goel, Shilpi, additional, McRonald, Fiona, additional, Antoniou, Antonis C, additional, Morris, Eva, additional, Hardy, Steven, additional, and Turnbull, Clare, additional

Published
2023
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16. A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation

Author

Qin, Chen, Moreno, Ricardo Guerrero, Bowles, Christopher, Ledig, Christian, Scheltens, Philip, Barkhof, Frederik, Rhodius-Meester, Hanneke, Tijms, Betty, Lemstra, Afina W., van der Flier, Wiesje M., Glocker, Ben, Rueckert, Daniel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Li, editor, Adeli, Ehsan, editor, Wang, Qian, editor, Shi, Yinghuan, editor, and Suk, Heung-Il, editor

Published
2016
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17. Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation

Author

Bowles, Christopher, Qin, Chen, Ledig, Christian, Guerrero, Ricardo, Gunn, Roger, Hammers, Alexander, Sakka, Eleni, Dickie, David Alexander, Hernández, Maria Valdés, Royle, Natalie, Wardlaw, Joanna, Rhodius-Meester, Hanneke, Tijms, Betty, Lemstra, Afina W., van der Flier, Wiesje, Barkhof, Frederik, Scheltens, Philip, Rueckert, Daniel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor

Published
2016
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18. Bleeding and thrombotic complications and their impact on mortality in patients supported with left ventricular assist device for cardiogenic shock.

Author

Bekono-Nessah, Ingrid, Rosenburg, Alex, Bowles, Christopher T, Riesgo-Gil, Fernando, Stock, Ulrich, Szydlo, Richard R, Laffan, Mike, and Arachchillage, Deepa J

Subjects
THROMBOSIS risk factors, THROMBOSIS, STATISTICS, PATIENT aftercare, SCIENTIFIC observation, CONFIDENCE intervals, MEDICAL device removal, AGE distribution, LOG-rank test, MULTIVARIATE analysis, BLOOD transfusion, HEART assist devices, RETROSPECTIVE studies, HEMOSTASIS, ANTICOAGULANTS, TERTIARY care, REGRESSION analysis, MULTIPLE organ failure, RISK assessment, TREATMENT effectiveness, CARDIOGENIC shock, ASPIRIN, ANEMIA, RESEARCH funding, DESCRIPTIVE statistics, KAPLAN-Meier estimator, SURVIVAL analysis (Biometry), MECHANICAL hearts, DATA analysis software, DEATH, HEMORRHAGE, PROBABILITY theory, LONGITUDINAL method, PROPORTIONAL hazards models, DISEASE complications
Abstract

Introduction: Thrombosis and bleeding are major complications in patients supported with left ventricular assist devices (LVADs). We aimed to assess the incidence of bleeding and thrombosis in patients supported with a HeartWare left ventricular assist device (HVAD), their predictive factors and their impact on mortality. Methods: A single centre retrospective observational study of patients supported with HVAD over 5 years from January 2015 to October 2020. Results: A total 139 patients (median age 52.5, 72.1% male) were included for analysis. The probability of 1-year survival was 73.1%. Advanced age (>60 years) and EuroSCORE II score (>20%) were independently associated with reduced survival. Major bleeding and thrombosis occurred in 46.8% and 35.3% respectively. Secondary mechanical circulatory support (MCS) increased likelihood of experiencing major bleeding (HR: 2.76, 95%1.65–4.62, p < 0.0001) whilst patients receiving aspirin were protected from bleeding and thrombosis (HR: 0.34 95% CI 0.19–0.58, p < 0.001). Pre-operative anaemia (HR: 3.02, 95% CI: 1.6–5.7, p = 0.014) and use of a secondary MCS device (HR: 2.78, 95% CI: 1.2–6.3, p = 0.001) were associated with an increased risk of thrombosis. Patients with any major bleeding (with or without thrombosis) had a 7.68-fold (95% CI 3.5–16.8) increased risk of death compared to those without. In contrast, 'thrombosis only' patients had 4.23-fold (95% CI 1.8–10.2) increased risk of death compared to those without thrombosis. The risk of mortality was increased in patients with any thrombosis and the risk of death was highest in patients with major bleeding and thrombosis (HR: 16.49 [95% CI 7.7–35.3]). Conclusions: Major bleeding and thrombosis significantly increase the 1-year mortality. Optimal perioperative haemostasis and anticoagulation remains crucial in patients supported with HVAD. [ABSTRACT FROM AUTHOR]

Published
2023
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20. An investigation into the flow structure of a generalised open channel intake

Author

Bowles, Christopher

Subjects
624, CFD, Run-of-the-river hydropower, Energy losses
Abstract

Open channel intake structures occur in many different applications such as navigable water courses, irrigation, power station facilities for cooling or steam production and run-of-the-river hydropower facilities. All these applications suffer from problems such as energy losses due to asymmetric flow patterns and the commonly associated problem of sedimentation. The flow structure and the associated energy losses at a generalised open channel intake structure are investigated using three-dimensional numerical and physical modelling techniques. Intake angles of 15, 30 and 45 degrees with flow split ratios (intake channel flow / main channel flow) ranging from 0.5 to 3.0 and Reynolds numbers ranging from 1x105 to 3x105 are investigated. A predictive hypothesis for the position and shape of the dividing streamplane is presented and tested with reasonable correlation. The hypothesis can be applied to channel width ratios of 0.5 (intake channel / main channel) with a plane, smooth, rectangular cross sectional area. The flow structure is also described using flow visualisation techniques in the physical model and streamline plotting, velocity vector and velocity profile graphics. A study is also undertaken to describe the turbulent kinetic energy in the flow domain. Predictions near the bed of the flow domain are poor in all analyses with improved results with depth towards the surface. Areas of secondary circulation cells, flow reversal and stagnation zones are identified by numerical and physical models. Validation of the numerical code is undertaken using three dimensional velocity and root mean square velocity data collected using the Acoustic Doppler Velocimeter (ADV). Calibration of the ADV is undertaken and the results closely match the manufacturers stated calibration data. The numerical code used is a finite volume method solving the Reynolds Averaged Navier Stokes equations and the k-s turbulence model. Validation of the code indicated that it was suitable for the investigation of the flow structure in this instance but failed to predict completely areas of high velocity gradients or flow reversal.

Published
1999

23. Algorithms to guide ambulance clinicians in the management of emergencies in patients with implanted rotary left ventricular assist devices

Author

Bowles, Christopher T, Hards, Rachel, Wrightson, Neil, Lincoln, Paul, Kore, Shishir, Marley, Laura, Dalzell, Jonathan R, Raj, Binu, Baker, Tracey A, Goodwin, Diane, Carroll, Petra, Pateman, Jane, Black, John J M, Kattenhorn, Paul, Faulkner, Mark, Parameshwar, Jayan, Butcher, Charles, Mason, Mark, Rosenberg, Alexander, McGovern, Ian, Weymann, Alexander, Gwinnutt, Carl, Banner, Nicholas R, Schueler, Stephan, Simon, Andre R, and Pitcher, David W

Published
2017
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24. Mechanical life support algorithm developed by simulation for inpatient emergency management of recipients of implantable left ventricular assist devices

Author

Akhtar, Waqas, primary, Gamble, Brigitte, additional, Kiff, Kristine, additional, Wypych-Zych, Agnieszka, additional, Raj, Binu, additional, Takata, Junko, additional, Gil, Fernando Riesgo, additional, Hurtado, Ana, additional, Rosenberg, Alex, additional, and Bowles, Christopher T., additional

Published
2022
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25. Supplemental Material - Bleeding and thrombotic complications and their impact on mortality in patients supported with left ventricular assist device for cardiogenic shock

Author

Bekono-Nessah, Ingrid, Rosenburg, Alex, Bowles, Christopher T, Riesgo-Gil, Fernando, Stock, Ulrich, Szydlo, Richard R, Laffan, Mike, and Arachchillage, Deepa J

Subjects
FOS: Clinical medicine, Cardiology, 110323 Surgery
Abstract

Supplemental Material for Bleeding and thrombotic complications and their impact on mortality in patients supported with left ventricular assist device for cardiogenic shock by Ingrid Bekono-Nessah, Alex Rosenburg, Christopher T Bowles, Fernando Riesgo-Gil, Ulrich Stock, Richard R Szydlo, Mike Laffan and Deepa J Arachchillage in Perfusion

Published
2022
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26. A novel intra-pericardial pulsatile device, reBEAT, for individualized, biventricular circulatory support without direct blood contact

Author

Schueler, Stephan, primary, Bowles, Christopher T., additional, Hinkel, Rabea, additional, Wohlfarth, Robert, additional, Schmid, Michael R., additional, Wildhirt, Stephen, additional, Stock, Ulrich, additional, Fischer, Johannes, additional, Reiser, Judith, additional, Kamla, Christine, additional, Tzekos, Konstantin, additional, Smail, Hassiba, additional, and de Vaal, Hamman, additional

Published
2022
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27. Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation

Author

Bowles, Christopher, primary, Qin, Chen, additional, Ledig, Christian, additional, Guerrero, Ricardo, additional, Gunn, Roger, additional, Hammers, Alexander, additional, Sakka, Eleni, additional, Dickie, David Alexander, additional, Hernández, Maria Valdés, additional, Royle, Natalie, additional, Wardlaw, Joanna, additional, Rhodius-Meester, Hanneke, additional, Tijms, Betty, additional, Lemstra, Afina W., additional, van der Flier, Wiesje, additional, Barkhof, Frederik, additional, Scheltens, Philip, additional, and Rueckert, Daniel, additional

Published
2016
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28. A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation

Author

Qin, Chen, primary, Moreno, Ricardo Guerrero, additional, Bowles, Christopher, additional, Ledig, Christian, additional, Scheltens, Philip, additional, Barkhof, Frederik, additional, Rhodius-Meester, Hanneke, additional, Tijms, Betty, additional, Lemstra, Afina W., additional, van der Flier, Wiesje M., additional, Glocker, Ben, additional, and Rueckert, Daniel, additional

Published
2016
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32. SDHC phaeochromocytoma and paraganglioma: A UK‐wide case series

Author

Williams, Sophie T., primary, Chatzikyriakou, Prodromos, additional, Carroll, Paul V., additional, McGowan, Barbara M., additional, Velusamy, Anand, additional, White, Gemma, additional, Obholzer, Rupert, additional, Akker, Scott, additional, Tufton, Nicola, additional, Casey, Ruth T., additional, Maher, Eamonn R., additional, Park, Soo‐Mi, additional, Porteous, Mary, additional, Dyer, Rebecca, additional, Tan, Tricia, additional, Wernig, Florian, additional, Brady, Angela F., additional, Kosicka‐Slawinska, Monika, additional, Whitelaw, Benjamin C., additional, Dorkins, Huw, additional, Lalloo, Fiona, additional, Brennan, Paul, additional, Carlow, Joseph, additional, Martin, Richard, additional, Mitchell, Anna L., additional, Harrison, Rachel, additional, Hawkes, Lara, additional, Newell‐Price, John, additional, Kelsall, Alan, additional, Igbokwe, Rebecca, additional, Adlard, Julian, additional, Schirwani, Schaida, additional, Davidson, Rosemarie, additional, Morrison, Patrick J., additional, Chung, Teng‐Teng, additional, Bowles, Christopher, additional, and Izatt, Louise, additional

Published
2021
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33. First Experience With the Synergy Micro-Pump in Patients in INTERMACS Class 1–2 as a Bridge to Transplantation: Pushing the Limits?

Author

Sabashnikov, Anton, Popov, Aron-Frederik, Bowles, Christopher T., Weymann, Alexander, Mohite, Prashant N., Wahlers, Thorsten, Wittwer, Thorsten, Zych, Bartlomiej, Garcia-Saez, Diana, Patil, Nikhil P., Fatullayev, Javid, Amrani, Mohamed, Banner, Nicholas R., Seidler, Tim, Unsoeld, Bernhard, Bireta, Christian, Schoendube, Friedrich A., and Simon, André R.

Published
2015
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34. The Lost Dark Age Kingdom of Rheged : the Discovery of a Royal Stronghold at Trusty’s Hill, Galloway

Author

Toolis, Ronan, Bowles, Christopher, Smith, Beverley Ballin, Ballin, Torben, Batt, Cathy, Blackwell, Alice, Campbell, Ewan, Cruickshanks, Gemma, Davis, Mary, Deighton, Karen, Evans, Jane, Forsyth, Katherine, Hamilton, Derek, Hamlet, Laura, Harris, Samuel, Hunter, Fraser, Pashley, Vanessa, Ramsay, Susan, Stern, Ben, Thickpenny, Cynthia, Toolis, Ronan, Bowles, Christopher, Smith, Beverley Ballin, Ballin, Torben, Batt, Cathy, Blackwell, Alice, Campbell, Ewan, Cruickshanks, Gemma, Davis, Mary, Deighton, Karen, Evans, Jane, Forsyth, Katherine, Hamilton, Derek, Hamlet, Laura, Harris, Samuel, Hunter, Fraser, Pashley, Vanessa, Ramsay, Susan, Stern, Ben, and Thickpenny, Cynthia

Published
2017

47. Medical image synthesis in the diagnosis and study of neurodegenerative diseases

Author

Bowles, Christopher, Reuckert, Daniel, and Engineering and Physical Sciences Research Council

Abstract

Medical imaging is a cornerstone of modern healthcare. The ability to acquire images from inside a patient has revolutionised the way doctors diagnose and treat diseases, with almost all clinical pipelines now involving imaging to some degree. The development of these imaging methods has led to the field of medical image computing, where a multitude of tools and techniques have been proposed to aid clinicians and researchers in interpreting and analysing these images. One such family of techniques involves generating synthetic medical images. Image synthesis techniques are wide and varied, ranging from basic phantoms, to disease atlases, to high resolution photo-realistic subject-specific images. Their applications are similarly diverse: for developing novel acquisition protocols, training and testing algorithms, visualising changes in disease, predicting particular image types from others, and improving image quality and resolution. This thesis examines the use of medical image synthesis with a particular focus on applications in neurodegenerative diseases. A method for synthesising subject-specific non-pathological images from pathological images is first proposed and used for the unsupervised brain lesion segmentation. We next show how generative adversarial networks (GANs) can be used to both analyse the structural changes seen in patients with Alzheimer's disease, and to add or remove these changes from patient images to produce a subject-specific prediction of disease progression. Finally we investigate how GAN-derived synthetic data can be used to increase the size of training datasets, and under what conditions this additional data can lead to an improvement across a variety of segmentation tasks. Within this context we explore two situations: where only a small amount of labelled data is available, and where a large amount of unlabelled data is also available. We show that the proposed methods can lead to significant improvements in segmentation results, especially when a small amount of labelled data is available. Open Access

Published
2018
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