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Keywords: Segmentation
MO-IePD-TRACK 3-1Autosegmentation On Low-Resolution T2-Weighted MRI of Head and Neck Cancers for Off-Line Dose Reconstruction in MR-Linac Adapt-To-Position Workflow
B McDonald1*, C Cardenas2, N O'connell3, S Ahmed4, M Naser5, J Xu6, D Thill7, R Zuhour8, S Mesko9, A Augustyn10, S Buszek11, S Grant12, B Chapman13, A Bagley14, K Wahid15, R He16, A Mohamed17, A Dresner18, J Christodouleas19, K Brock20, C Fuller21, (1) UT MD Anderson Cancer Center, Houston, TX, (2) UT MD Anderson Cancer Center, Houston, TX, (3) Elekta Inc., ,,(4) UT MD Anderson Cancer Center, ,,(5) UT MD Anderson Cancer Center, Houston, ,(6) Elekta Inc., St. Charles, MO, (7) Elekta Inc., St. Charles, MO, (8) University Of Texas Medical Branch, ,,(9) UT MD Anderson Cancer Center, ,,(10) UT MD Anderson Cancer Center, ,,(11) UT MD Anderson Cancer Center, ,,(12) UT MD Anderson Cancer Center, ,,(13) UT MD Anderson Cancer Center, ,,(14) UT MD Anderson Cancer Center, ,,(15) UT MD Anderson Cancer Center, ,,(16) UT MD Anderson Cancer Center, Houston, TX, (17) UT MD Anderson Cancer Center, Houston, TX, (18) Philips Healthcare, ,,(19) Elekta Inc., ,,(20) UT MD Anderson Cancer Center, Houston, TX, (21) UT MD Anderson Cancer Center, Houston, TX
MO-IePD-TRACK 6-3Liver Segmentation Workflow Development for Pre-Treatment Hepatic Y90 Resin Microspheres Therapy Using a Commercially Available Software
D Alvarez1,2*, N Ridha3, A Gutierrez1,2, M Chuong1,2, R Gandhi1,2,4, (1) Miami Cancer Institute, Baptist Health South Florida, Miami, FL, (2) Florida International University, Herbert Wertheim College of Medicine, Miami, FL, (3) MIM Software Inc., Cleveland, OH,(4) Miami Cardiac & Vascular Institute, Miami, FL
MO-IePD-TRACK 6-4Demonstration of An Automatic Segmentation Method for Evaluating Cardiac Structure Doses in Support of Research On Cardiovascular Morbidity Subsequent to Breast Radiotherapy
M Mille1, J Jung2*, B Ky3, W Kenworthy3, C Lee4, Y Yeom1, A Kwag5, W Bosch6, S MacDonald7, O Cahlon8, J Bekelman9, C Lee1, (1) National Cancer Institute, Bethesda, MD, (2) East Carolina University, Greenville, NC, (3) University of Pennsylvania, Philadelphia, PA, (4) University of Michigan, Ann Arbor, MI, (5) Vanderbilt University, Nashville, TN, (6) Washington University, Saint Louis, MO, (7) Massachusetts General Hospital, Boston, MA, (8) Memorial Sloan Kettering Cancer Center, New York, NY, (9) University of Pennsylvania, Philadelphia, PA
PO-GePV-E-13Incorporating Statistical Learning and Artificial Intelligence Education to Medical Physics Graduate Training
C Cardenas*, L Court, University of Texas MD Anderson Cancer Center, Houston, TX
PO-GePV-M-4Automatic Contouring QA Approach Using a Deep-Learning-Based Auto-Contouring System
D Rhee1*, C Anakwenze1, B Rigaud1, A Jhingran1, C Cardenas1, L Zhang1, S Prajapati1, S Kry1, K Brock1, B Beadle2, W Shaw3, D O'reilly3, J Parkes4, H Burger4, N Fakie4, C Trauernicht5, H Simonds5, L Court1, (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) Stanford University, Stanford, CA, (3) University Of The Free State, Bloemfontein, ZA, (4) University Of Cape Town, Cape Town, ZA, (5) Stellenbosch University, Stellenbosch, ZA,
PO-GePV-M-5Radiation Induced Lung Damage Tissue Segmentation and Classification Framework
A Szmul1*, E Chandy1,4, C Veiga1, A Stavropoulou1, J Jacob1,2, D Landau3, C Hiley4, J McClelland1, (1) Centre for Medical Image Computing, University College London, London, UK, (2) Department of Respiratory Medicine, University College London, London, UK, (3) Guys & St Thomas NHS Foundation Trust, London, UK, (4) Cancer Institute, UCL, London, UK
PO-GePV-M-6Hybrid Approach of Auto-Segmentation Based On Pixel Level Edge Detection
D Tewatia1*, R Tolakanahalli2, RW Pyzalski3, (1) University of Wisconsin-Madison, Madison, WI, (2) Miami Cancer Institute, Miami, FL, (3) Retiree of University Of Wisconsin-Madison, Madison, WI
PO-GePV-M-7Performance of a Convolutional Neural Network-Based Algorithm for Automatic Segmentation of the Whole Heart in Non-Contrast Radiotherapy Planning CTs with Image Artifacts
D Mehta1*, B Park2, J Jung3, M Mille4, B Ky5, W Kenworthy6, C Lee7, Y Yeom8, W Bosch9, S MacDonald10, O Cahlon11, J Bekelman12, C Lee13, (1) East Carolina University, (2) East Carolina University, (3) East Carolina University, Greenville, NC, (4) National Cancer Institute, Bethesda, MD, (5) University of Pennsylvania, Philadelphia, PA, (6) University of Pennsylvania, Philadelphia, PA, (7) University of Michigan, Ann Arbor, MI, (8) National Cancer Institute, Rockville, MD, (9) Washington University, Saint Louis, MO, (10) (11) New York, NY, (12) University of Penn, Philadelphia, PA, (13) National Cancer Institute, Rockville, MD
PO-GePV-M-8A 3D Multi-Modality Lung Tumor Segmentation Method Based On Deep Learning
S Wang1*, L Yuan2, E Weiss3, R Mahon4, (1) Virginia Commonwealth University, Richmond, VA, (2) Virginia Commonwealth University Medical Center, Richmond, VA, (3) Virginia Commonwealth University, Richmond, VA, (4) Washington University in St. Louis, St. Louis, MO
PO-GePV-M-38What's Behind Auto-Segmentation Models: An Interpretability Analysis of Calculation Logic of Segmentation Model for Brain Tumor
H Chen1*, D Ban2, X Qi3, (1) Xi'an University of Posts and telecommunications, Xi'an, 61, CN, (2) Xi'an University of Posts and telecommunications, Xi'an, 61, CN, (3) UCLA School of Medicine, Los Angeles, CA
PO-GePV-M-41Automatic Lung Field Segmentation On Chest Radiographs of COVID-19 Patients to Improve Diagnostic Deep Learning
G Schlafly*, Q Hu, F Li, K Drukker, J Fuhrman, M Giger, University of Chicago, Chicago, IL
PO-GePV-M-139Multimodality Image Registration for Application in Brachytherapy Based On Automated Organ Segmentation
K Qing1*, X Feng2,3, S Glaser1, W Watkins1, D Du1, Y Chen1, C Han1, J Liang1, J Liu1, B Liu3, Q Chen3,4, A Liu1, (1) City of Hope National Medical Center, Duarte, CA, (2) University of Virginia, Charlottesville, VA, (3) Carina Medical, Lexington, KY , (4) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (5) University of Kentucky, Lexington, KY
PO-GePV-M-157Towards Real-Time Markerless Prostate IGRT During VMAT Treatment
A Mylonas1,2*, M Mueller1,2, P Keall1, J Booth3, D Nguyen1,2,3, (1) ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, AU, (2) School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, AU, (3) Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, AU
PO-GePV-M-164Auto-Segmentation of Organs at Risk for CT Thoracic Cases: A Single Institution's Experience
D Darkow1*, B Lee1, J Seger-Paisley2, R Woods2, U Duong2, A Houser2, J Paisley2, 3, M Butler1, A Kruzer1, A Nelson1, (1) MIM Software Inc. (2) New Hanover Regional Medical Center (3) Coastal Carolina Radiation Oncology
PO-GePV-M-165Deep Learning-Based Auto-Segmentation of Glioblastoma in Brain Cancer Radiotherapy
S Sadeghi*, S Gholami,
PO-GePV-M-167PTV Auto Contouring for Prostate and Nodes Utilizing Deep Learning Artificial Intelligence
H Yao*, J Chang, J Baker, Northwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, New York
PO-GePV-M-168Synthetic Lung MRI for Auto-Segmentation Using Unet Training: Supervised Cross-Domain Adaptation From CT to MRI
Y Liu*, J Jiang, N Tyagi, H Veeraraghavan, G Li, J Deasy, L Cervino, Memorial Sloan Kettering Cancer Center, New York, NY
PO-GePV-M-169Prior Information Guided Auto-Contouring of Breast Tumor Bed for Post-Operative Breast Cancer Radiotherapy
X Xie*, H Yan, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
PO-GePV-M-216Head and Neck Multi-Organ Segmentation On Dual-Energy CT Using Dual Pyramid Convolutional Neural Networks
T Wang*, Y Lei, J Roper, B Ghavidel, J Beitler, M McDonald, J Bradley, T Liu, X Yang, Emory Univ, Atlanta, GA
PO-GePV-M-243Multi-Scale Deep Learning CT Liver and Spleen Segmentation Network for Radiation-Induced Toxicity Outcomes Analysis
R Haq*, I Onochie, A Apte, M Thor, J Deasy, Memorial Sloan-Kettering Cancer Center, New York, NY
PO-GePV-M-250Efficient and Stable Evaluation of Image Registration Using Deep Learning
B Yang*, X Chen, J Zhu, K Men, J Dai, Cancer hospital Chinese academy of medical sciences, Beijing, 11CN,
PO-GePV-M-257A Deep Learning-Based Physician-Specific OAR Segmentation Framework for Radiotherapy Treatment Planning
Y Chen*, L Xing, L Yu, N Panjwani, J Obeid, M Gensheimer, H Bagshaw, M Buyyounouski, N Kovalchuk, B Han, Stanford University School of Medicine, Stanford, CA
PO-GePV-M-263Using New Feature Extraction Framework to Predict Heart Toxicity in Breast Cancer
B Choi1*, S Yoo1,J Moon1, J Kim1, J Chang1, H Kim1, (1) Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul
PO-GePV-T-23Dosimetric Impact of Atlas Generated Contours for Post-Prostate Seed Implant Evaluation
A Antolak*, J Ciezki, M Kolar, Cleveland Clinic Foundation, Cleveland, OH
PO-GePV-T-28Evaluation of Eclipse Smart Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy
J Li*, R Anne, Thomas Jefferson University, Philadelphia, PA
PO-GePV-T-269Analyzing Performance of Atlas-Based and Deep Learning-Based Auto-Segmentation in Breast Cancer Patients
M Weiland1*, K Kisling1, (1) UC San Diego, La Jolla, CA
SU-B-TRACK 6-1A Fully Automated Approach for Labeling, Contouring, and Treatment Planning of Vertebral Bodies, Including Automatic Verification to Prevent Errors
T Netherton*, C Nguyen, C Cardenas, C Chung, A Klopp, L Colbert, D Rhee, C Peterson, R Howell, P Balter, L Court, MD Anderson Cancer Center, Houston, TX
SU-B-TRACK 6-3Evaluating the Clinical Acceptability of Deep Learning Contours in Prostate Radiotherapy
J Duan*, M Bernard, B Willows, L Downes, W Mourad, W St Clair, X Feng, Q Chen, University of Kentucky, Lexington, KY
SU-CD-TRACK 5-0Advances in Automatic Segmentation, Treatment Planning and QA
H Veeraraghavan1*, Q Wu2*, E Ford3*, S Jiang4*, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Duke University Medical Center, Durham, NC, (3) University of Washington, Seattle, WA, (4) UT Southwestern Medical Center, Dallas, TX
SU-CD-TRACK 5-1Deep Learning Automated Segmentation for Radiation Therapy Planning
H Veeraraghavan1*, Q Wu2*, E Ford3*, S Jiang4*, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Duke University Medical Center, Durham, NC, (3) University of Washington, Seattle, WA, (4) UT Southwestern Medical Center, Dallas, TX
SU-CD-TRACK 5-2Automation in Treatment Planning Process
H Veeraraghavan1*, Q Wu2*, E Ford3*, S Jiang4*, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Duke University Medical Center, Durham, NC, (3) University of Washington, Seattle, WA, (4) UT Southwestern Medical Center, Dallas, TX
SU-CD-TRACK 5-3Advances in Quality Assurance Through Automation
H Veeraraghavan1*, Q Wu2*, E Ford3*, S Jiang4*, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Duke University Medical Center, Durham, NC, (3) University of Washington, Seattle, WA, (4) UT Southwestern Medical Center, Dallas, TX
SU-CD-TRACK 5-4Thoughts on The Clinical Implementation of AI Tools In Treatment Planning Process
H Veeraraghavan1*, Q Wu2*, E Ford3*, S Jiang4*, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Duke University Medical Center, Durham, NC, (3) University of Washington, Seattle, WA, (4) UT Southwestern Medical Center, Dallas, TX
SU-F-TRACK 6-1Automatic Stent Recognition Using Deep Neural Network for Quantitative Intra-Fractional Motion Monitoring in Pancreatic Cancer Radiotherapy
X He*, W Cai, F Li, P Zhang, L Cervino, T Li, X Li, Memorial Sloan Kettering Cancer Center, New York, NY
SU-IePD-TRACK 1-4Wavelet U-Net: Incorporating Wavelet Transform Into U-Net for Liver Segmentation
J Chang*, C Chang, California Protons Cancer Therapy Center / UCSD, San Diego, CA
SU-IePD-TRACK 3-2Multi-Organ Segmentation On An Inference-Combined Dataset
T Marschall1*, X Li2, K Yang3, B Liu4, (1) Massachusetts General Hospital, Cambridge, MA, (2) Massachusetts General Hospital, Belmont, MA, (3) Harvard Medical School, Massachusetts General Hospital, Boston, MA, (4) Massachusetts General Hospital, Boston, MA
SU-IePD-TRACK 3-3Segmentation of a Highly Deformable Organ Using a Machine-Assisted Interpolation Algorithm
D Luximon*, Y Abdulkadir, E Morris, P Chow, J Lamb, University of California, Los Angeles, Los Angeles, CA
SU-IePD-TRACK 3-4A Deep Learning Approach for Contour Interpolation
D Yang2*,C Zhao1, Y Duan1, H Li2, H Kim2, L Henke2, D Yang2, (1) University of Missouri, (2) Washington University School of Medicine, St. Louis, MO
TH-D-TRACK 3-2Automated Tumor Localization and Segmentation Through Hybrid Neural Network in Head & Neck Cancer
A Qasem1*, Z Zhou2, (1) ,Warrensburg, MO, (2) University of Central Missouri, Warrensburg, MISSOURI
TH-D-TRACK 3-3Deep Siamese Network for False Positive Reduction in Brain Metastases Segmentation
Z Yang*, M Chen, R Timmerman, T Dan, Z Wardak, W Lu, X Gu, UT Southwestern Medical Center, Dallas, TX
TH-D-TRACK 3-5Unsupervised COVID-19 Pneumonia Lesion Segmentation in CT Scans Using Cycle Consistent Generative Adversarial Network
Y Liu*, C Fang*, J Wen, Y Yang, University of Science and Technology of China, Hefei, Anhui, P. R. China * The authors contribute equally
TH-E-TRACK 4-1Few Shot Meta Learner for Post-Operative Prostate CTV Style Adaptation
A Balagopal*, D Nguyen, T Bai, H Morgan, M Dohopolski, N Desai, A Garant, R Hannan, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX
TH-E-TRACK 4-2Multi-Year Clinical Experience with In-House Developed AI Auto-Segmentation for Radiotherapy Planning
S Elguindi1*, J Jiang1, A Apte1, A Iyer1, E LoCastro1, Y Hu1, E Cha2, E Gillespie2, I Onochie2, D Gorovets2, M Zelefsky2, S Berry1, M Thor1, J Deasy1, L Cervino1, H Veeraraghavan1, (1) Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States (2) Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
TH-E-TRACK 4-5Small Convolutional Neural Networks for Efficient 3D Medical Image Segmentation
A Celaya1, J Actor1,2, R Muthusivarajan1, E Gates1*, C Chung1, D Schellingerhout1, B Riviere2, D Fuentes1, (1) University Of Texas Md Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX
TH-IePD-TRACK 3-2Investigation of Clinical Target Volume Segmentation for Whole Breast Irradiation Using 3D Convolutional Neural Network and the Shape Regularization Model Without the Prior Information of the Target-Side Breast
M Oya1*, S Sugimoto2, (1) Juntendo University Graduate School of Medicine, Tokyo, JP, (2) Juntendo University, Tokyo, JP
TH-IePD-TRACK 3-4Segmentation by Individualized Registration (SIR) for CBCT-Based Adaptive Radiation Therapy
X Liang1*, J Chun2, H Morgan1, T Bai1, D Nguyen1, J Park1, S Jiang1, (1) Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, (2) Department of Radiation Oncology, Yonsei University, Seoul, KR
TH-IePD-TRACK 3-5Intentional Deep Overfit Learning (IDOL): An Application to CT Segmentation-Based Daily Adaptive Radiotherapy
Byongsu Choi1,4, Inkyung Park1,5, Jaehee Chun1,4, Sven Olberg1,2,3, Jinsung Kim4,Yang Kyun Park1, Mu-han Lin1, Bin Cai1, Steve Jiang1, Justin C. Park1, (1) Department of Radiation Oncology, The University of Texas Southwestern Medical Center (2) Department of Radiation Oncology, Washington University in St. Louis, St Louis, MO 63110 (3) Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO 63110 (4) Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea (5) Department of Integrative Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
TH-IePD-TRACK 3-6Deep Learning Neural Network for Automatic Delineation of Para-Aortic Clinical Target Volume
W Lu*, P DCunha, W Chi, M Chen, L Ma, M Kazemimoghadam, Z Yang, X Gu, K Albuquerque, University of Texas Southwestern Medical Center, Dallas, TX
TU-E-TRACK 6-3GAN-Driven Anomaly Detection for Active Learning in Medical Imaging Segmentation
M Woodland*1,2, A Patel2,3, B Anderson1, E Lin1, E Koay1, B Odisio1, K Brock1, (1) The University of Texas MD Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX, (3) Baylor College of Medicine, Houston, TX
TU-E-TRACK 6-6Simultaneous Segmentation of Target and Organ at Risk in Thoracic Kilovoltage Images
M Mueller1,2*, A Mylonas1,2, P Keall1, J Booth3, D Nguyen1,2,3, (1) ACRF Image X Institute, Sydney, NSW, AU, (2) University of Technology Sydney, Ultimo, NSW, AU (3) Royal North Shore Hospital, Sydney, NSW, AU
TU-E-TRACK 6-7Towards Artificial Intelligence and Clinician Integrated Systems (AICIS): Interactive Contour Revision with Deep Boundary Net
T Bai*, A Balagopal, M Dohopolski, H Morgan, R McBeth, J Tan, D Nguyen, S Jiang, UT Southwestern Medical Center, Dallas, TX
TU-F-TRACK 6-13D Dense U-Net for Fully Automated Multi-Organ Segmentation in Female Pelvic Magnetic Resonance Imaging
F Zabihollahy*, E Schmidt, A Viswanathan, J Lee, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
TU-F-TRACK 6-4Auto-Segmentation of Organs-At-Risk in Head and Neck CT Images with Dual Shape Guided Network
S Wang*, T Yanagihara, B Chera, C Shen, P Yap, J Lian, UNC at Chapel Hill, Chapel Hill, NC
TU-IePD-TRACK 4-1A Deep Reinforcement Learning Based Pipeline for Prostate Segmentation On MRI with Low Variance Performance
L Xu1*, W Shi1, N Wen2, (1) Wayne State University, Detroit, MI, (2) Henry Ford Health System, Detroit, MI
TU-IePD-TRACK 4-4Improving Deep Learning Auto-Segmentation Using An Adaptive Spatial Resolution Approach
A Amjad1*, J Xu2, D Thill2, M Awan1, M Shukla1, W Hall1, B Erickson1, X Li1, (1) Medical College of Wisconsin, Milwaukee, WI, (2) Elekta Inc., MO, USA
TU-IePD-TRACK 4-5On the Application of a Variational Autoencoder (VAE) and Transfer Learning to Account for Inter-Observer Uncertainties in Automatic Prostate Gland Segmentation
H Bagher-Ebadian*1,2, X Li3, E Mohamed1,2, B Movsas1,2, D Zhu3, IJ Chetty1,2, (1) Henry Ford Health System, Detroit, MI, (2) Henry Ford Cancer Institute, Detroit, MI, (3) Wayne State University, Detroit, MI
TU-IePD-TRACK 4-6Deploying Deep Learning-Based Image Segmentation Models Via CERR
A Iyer*, E LoCastro, J Deasy, A Apte, Memorial Sloan-Kettering Cancer Center, New York, NY
TU-IePD-TRACK 6-2Development of a Quantitative Method to Evaluate Organ Segmentation for Enhanced Error Detection
E Pryser*, F Reynoso, M Schmidt, G Hugo, N Knutson, Washington University School of Medicine, St. Louis, MO
WE-D-TRACK 1-1Towards Contour Quality Assurance of Cardiac Structures with Automatic Segmentation
C Uche1, H Geng2, J Yu3, E Gore4, Y Xiao5, (1) University of Pennsylvania, Philadelphia, PA, (2) University Of Pennsylvania, Philadelphia, PA,(3) NRG Oncology Statistics and Data Management Center, Philadelphia, PA,(4) Medical College of Wisconsin, Milwaukee, Wisconsin,(5) University of Pennsylvania, Philadelphia, PA
WE-D-TRACK 6-1Auto-Correction of Inaccurate Auto-Segmentation for MRI-Guided Adaptive Radiotherapy
J Ding1*, Y Zhang1, A Amjad1, J Xu2, D Thill2, X Li1, (1) Medical College of Wisconsin, Milwaukee, WI, (2) Elekta Inc., St. Charles, MO
WE-D-TRACK 6-6Learn From What You Drew - a Deep Learning Assisted Fast Semi-Automatic Segmentation for Complex Anatomy On MRI
Y Zhang*, Y Liang, A Amjad, E Ahunbay, E Paulson, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI
WE-IePD-TRACK 3-6Comparison of 3D Deep Convolutional Neural Networks and Training Strategies for Ventilated Lung Segmentation Using Multi-Nuclear Hyperpolarized Gas MRI
J Astley*, A Biancardi, P Hughes, L Smith, H Marshall, J Eaden, N Weatherley, G Collier, J Wild, B Tahir, The University of Sheffield, Sheffield, United Kingdom

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