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675 results on '"Piechnik, Stefan K."'

1. Cardiovascular Magnetic Resonance Before Invasive Coronary Angiography in Suspected Non–ST-Segment Elevation Myocardial Infarction

Author

Shanmuganathan, Mayooran, Nikolaidou, Chrysovalantou, Burrage, Matthew K., Borlotti, Alessandra, Kotronias, Rafail, Scarsini, Roberto, Banerjee, Abhirup, Terentes-Printzios, Dimitrios, Pitcher, Alex, Gara, Edit, Langrish, Jeremy, Lucking, Andrew, Choudhury, Robin, De Maria, Giovanni Luigi, Banning, Adrian, Piechnik, Stefan K., Channon, Keith M., and Ferreira, Vanessa M.

Published
2024
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3. Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

Author

Puyol-Anton, Esther, Ruijsink, Bram, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Razavi, Reza, and King, Andrew P.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias. Most applications to date have been in computer vision, although some work in healthcare has started to emerge. The use of deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, and such techniques are starting to be translated into clinical practice. However, no work has yet investigated the fairness of such models. In this work, we perform such an analysis for racial/gender groups, focusing on the problem of training data imbalance, using a nnU-Net model trained and evaluated on cine short axis cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from 6 different racial groups. We find statistically significant differences in Dice performance between different racial groups. To reduce the racial bias, we investigated three strategies: (1) stratified batch sampling, in which batch sampling is stratified to ensure balance between racial groups; (2) fair meta-learning for segmentation, in which a DL classifier is trained to classify race and jointly optimized with the segmentation model; and (3) protected group models, in which a different segmentation model is trained for each racial group. We also compared the results to the scenario where we have a perfectly balanced database. To assess fairness we used the standard deviation (SD) and skewed error ratio (SER) of the average Dice values. Our results demonstrate that the racial bias results from the use of imbalanced training data, and that all proposed bias mitigation strategies improved fairness, with the best SD and SER resulting from the use of protected group models., Comment: MICCAI 2021 conference

Published
2021

4. Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study

Author

Ng, Matthew, Guo, Fumin, Biswas, Labonny, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Wright, Graham

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review., Comment: Accepted to IEEE Transactions on Biomedical Engineering. Copyright (c) 2022 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org

Published
2020
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5. Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

Author

Ferdian, Edward, Suinesiaputra, Avan, Fung, Kenneth, Aung, Nay, Lukaschuk, Elena, Barutcu, Ahmet, Maclean, Edd, Paiva, Jose, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E, and Young, Alistair A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional study, 4508 cases from the UK Biobank were split randomly into 3244 training and 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of 1) a convolutional neural network (CNN) for localization, and 2) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis. Results: Within the test set, myocardial end-systolic circumferential Green strain errors were -0.001 +/- 0.025, -0.001 +/- 0.021, and 0.004 +/- 0.035 in basal, mid, and apical slices respectively (mean +/- std. dev. of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in diabetics, hypertensives, and participants with previous heart attack. Typical processing time was ~260 frames (~13 slices) per second on an NVIDIA Tesla K40 with 12GB RAM, compared with 6-8 minutes per slice for the manual analysis. Conclusions: The fully automated RNNCNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack., Comment: accepted in Radiology Cardiothoracic Imaging

Published
2020
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6. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Author

Raman, Betty, McCracken, Celeste, Cassar, Mark P, Moss, Alastair J, Finnigan, Lucy, Samat, Azlan Helmy A, Ogbole, Godwin, Tunnicliffe, Elizabeth M, Alfaro-Almagro, Fidel, Menke, Ricarda, Xie, Cheng, Gleeson, Fergus, Lukaschuk, Elena, Lamlum, Hanan, McGlynn, Kevin, Popescu, Iulia A, Sanders, Zeena-Britt, Saunders, Laura C, Piechnik, Stefan K, Ferreira, Vanessa M, Nikolaidou, Chrysovalantou, Rahman, Najib M, Ho, Ling-Pei, Harris, Victoria C, Shikotra, Aarti, Singapuri, Amisha, Pfeffer, Paul, Manisty, Charlotte, Kon, Onn M, Beggs, Mark, O'Regan, Declan P, Fuld, Jonathan, Weir-McCall, Jonathan R, Parekh, Dhruv, Steeds, Rick, Poinasamy, Krisnah, Cuthbertson, Dan J, Kemp, Graham J, Semple, Malcolm G, Horsley, Alexander, Miller, Christopher A, O'Brien, Caitlin, Shah, Ajay M, Chiribiri, Amedeo, Leavy, Olivia C, Richardson, Matthew, Elneima, Omer, McAuley, Hamish J C, Sereno, Marco, Saunders, Ruth M, Houchen-Wolloff, Linzy, Greening, Neil J, Bolton, Charlotte E, Brown, Jeremy S, Choudhury, Gourab, Diar Bakerly, Nawar, Easom, Nicholas, Echevarria, Carlos, Marks, Michael, Hurst, John R, Jones, Mark G, Wootton, Daniel G, Chalder, Trudie, Davies, Melanie J, De Soyza, Anthony, Geddes, John R, Greenhalf, William, Howard, Luke S, Jacob, Joseph, Man, William D-C, Openshaw, Peter J M, Porter, Joanna C, Rowland, Matthew J, Scott, Janet T, Singh, Sally J, Thomas, David C, Toshner, Mark, Lewis, Keir E, Heaney, Liam G, Harrison, Ewen M, Kerr, Steven, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Sheikh, Aziz, Zheng, Bang, Jenkins, R Gisli, Cox, Eleanor, Francis, Susan, Halling-Brown, Mark, Chalmers, James D, Greenwood, John P, Plein, Sven, Hughes, Paul J C, Thompson, A A Roger, Rowland-Jones, Sarah L, Wild, James M, Kelly, Matthew, Treibel, Thomas A, Bandula, Steven, Aul, Raminder, Miller, Karla, Jezzard, Peter, Smith, Stephen, Nichols, Thomas E, McCann, Gerry P, Evans, Rachael A, Wain, Louise V, Brightling, Christopher E, Neubauer, Stefan, Baillie, J K, Shaw, Alison, Hairsine, Brigid, Kurasz, Claire, Henson, Helen, Armstrong, Lisa, Shenton, Liz, Dobson, H, Dell, Amanda, Lucey, Alice, Price, Andrea, Storrie, Andrew, Pennington, Chris, Price, Claire, Mallison, Georgia, Willis, Gemma, Nassa, Heeah, Haworth, Jill, Hoare, Michaela, Hawkings, Nancy, Fairbairn, Sara, Young, Susan, Walker, S, Jarrold, I, Sanderson, Amy, David, C, Chong-James, K, Zongo, O, James, W Y, Martineau, A, King, Bernie, Armour, C, McAulay, D, Major, E, McGinness, Jade, McGarvey, L, Magee, N, Stone, Roisin, Drain, S, Craig, T, Bolger, A, Haggar, Ahmed, Lloyd, Arwel, Subbe, Christian, Menzies, Daniel, Southern, David, McIvor, Emma, Roberts, K, Manley, R, Whitehead, Victoria, Saxon, W, Bularga, A, Mills, N L, El-Taweel, Hosni, Dawson, Joy, Robinson, Leanne, Saralaya, Dinesh, Regan, Karen, Storton, Kim, Brear, Lucy, Amoils, S, Bermperi, Areti, Elmer, Anne, Ribeiro, Carla, Cruz, Isabel, Taylor, Jessica, Worsley, J, Dempsey, K, Watson, L, Jose, Sherly, Marciniak, S, Parkes, M, McQueen, Alison, Oliver, Catherine, Williams, Jenny, Paradowski, Kerry, Broad, Lauren, Knibbs, Lucy, Haynes, Matthew, Sabit, Ramsey, Milligan, L, Sampson, Claire, Hancock, Alyson, Evenden, Cerys, Lynch, Ceri, Hancock, Kia, Roche, Lisa, Rees, Meryl, Stroud, Natalie, Thomas-Woods, T, Heller, S, Robertson, E, Young, B, Wassall, Helen, Babores, M, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Price, Carly, Beranova, Eva, Ramos, Hazel, Weston, Heather, Deery, Joanne, Austin, Liam, Solly, Reanne, Turney, Sharon, Cosier, Tracey, Hazelton, Tracy, Ralser, M, Wilson, Ann, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, McCormick, W, Dewar, A, Arbane, Gill, Kaltsakas, G, Kerslake, Helen, Rossdale, J, Bisnauthsing, Karen, Aguilar Jimenez, Laura A, Martinez, L M, Ostermann, Marlies, Magtoto, Murphy M, Hart, Nicholas, Marino, Philip, Betts, Sarah, Solano, Teresa S, Arias, Ava Maria, Prabhu, A, Reed, Annabel, Wrey Brown, Caroline, Griffin, Denise, Bevan, Emily, Martin, Jane, Owen, J, Alvarez Corral, Maria, Williams, Nick, Payne, Sheila, Storrar, Will, Layton, Alison, Lawson, Cathy, Mills, Clare, Featherstone, James, Stephenson, Lorraine, Burdett, Tracy, Ellis, Y, Richards, A, Wright, C, Sykes, D L, Brindle, K, Drury, Katie, Holdsworth, L, Crooks, M G, Atkin, Paul, Flockton, Rachel, Thackray-Nocera, Susannah, Mohamed, Abdelrahman, Taylor, Abigail, Perkins, Emma, Ross, Gavin, McGuinness, Heather, Tench, Helen, Phipps, Janet, Loosley, Ronda, Wolf-Roberts, Rebecca, Coetzee, S, Omar, Zohra, Ross, Alexandra, Card, Bethany, Carr, Caitlin, King, Clara, Wood, Chloe, Copeland, D, Calvelo, Ellen, Chilvers, Edwin R, Russell, Emily, Gordon, Hussain, Nunag, Jose Lloyd, Schronce, J, March, Katherine, Samuel, Katherine, Burden, L, Evison, Lynsey, McLeavey, Laura, Orriss-Dib, Lorna, Tarusan, Lawrence, Mariveles, Myril, Roy, Maura, Mohamed, Noura, Simpson, Neil, Yasmin, Najira, Cullinan, P, Daly, Patrick, Haq, Sulaimaan, Moriera, Silvia, Fayzan, Tamanah, Munawar, Unber, Nwanguma, Uchechi, Lingford-Hughes, A, Altmann, Danny, Johnston, D, Mitchell, J, Valabhji, J, Price, L, Molyneaux, P L, Thwaites, Ryan S, Walsh, S, Frankel, A, Lightstone, L, Wilkins, M, Willicombe, M, McAdoo, S, Touyz, R, Guerdette, Anne-Marie, Warwick, Katie, Hewitt, Melanie, Reddy, R, White, Sonia, McMahon, A, Hoare, Amy, Knighton, Abigail, Ramos, Albert, Te, Amelie, Jolley, Caroline J, Speranza, Fabio, Assefa-Kebede, Hosanna, Peralta, Ida, Breeze, Jonathon, Shevket, K, Powell, Natassia, Adeyemi, Oluwaseun, Dulawan, Pearl, Adrego, Rita, Byrne, S, Patale, Sheetal, Hayday, A, Malim, M, Pariante, C, Sharpe, C, Whitney, J, Bramham, K, Ismail, K, Wessely, S, Nicholson, T, Ashworth, Andrew, Humphries, Amy, Tan, Ai Lyn, Whittam, Beverley, Coupland, C, Favager, Clair, Peckham, D, Wade, Elaine, Saalmink, Gwen, Clarke, Jude, Glossop, Jodie, Murira, Jennifer, Rangeley, Jade, Woods, Janet, Hall, Lucy, Dalton, Matthhew, Window, Nicola, Beirne, Paul, Hardy, Tim, Coakley, G, Turtle, Lance, Berridge, Anthony, Cross, Andy, Key, Angela L, Rowe, Anna, Allt, Ann Marie, Mears, Chloe, Malein, Flora, Madzamba, Gladys, Hardwick, H E, Earley, Joanne, Hawkes, Jenny, Pratt, James, Wyles, J, Tripp, K A, Hainey, Kera, Allerton, Lisa, Lavelle-Langham, L, Melling, Lucy, Wajero, Lilian O, Poll, L, Noonan, Matthew J, French, N, Lewis-Burke, N, Williams-Howard, S A, Cooper, Shirley, Kaprowska, Sabina, Dobson, S L, Marsh, Sophie, Highett, Victoria, Shaw, V, Beadsworth, M, Defres, S, Watson, Ekaterina, Tiongson, Gerlynn F, Papineni, Padmasayee, Gurram, Sambasivarao, Diwanji, Shalin N, Quaid, Sheena, Briggs, A, Hastie, Claire, Rogers, Natalie, Stensel, D, Bishop, L, McIvor, K, Rivera-Ortega, P, Al-Sheklly, B, Avram, Cristina, Faluyi, David, Blaikely, J, Piper Hanley, K, Radhakrishnan, K, Buch, M, Hanley, N A, Odell, Natasha, Osbourne, Rebecca, Stockdale, Sue, Felton, T, Gorsuch, T, Hussell, T, Kausar, Zunaira, Kabir, T, McAllister-Williams, H, Paddick, S, Burn, D, Ayoub, A, Greenhalgh, Alan, Sayer, A, Young, A, Price, D, Burns, G, MacGowan, G, Fisher, Helen, Tedd, H, Simpson, J, Jiwa, Kasim, Witham, M, Hogarth, Philip, West, Sophie, Wright, S, McMahon, Michael J, Neill, Paula, Dougherty, Andrew, Morrow, A, Anderson, David, Grieve, D, Bayes, Hannah, Fallon, K, Mangion, K, Gilmour, L, Basu, N, Sykes, R, Berry, C, McInnes, I B, Donaldson, A, Sage, E K, Barrett, Fiona, Welsh, B, Bell, Murdina, Quigley, Jackie, Leitch, Karen, Macliver, L, Patel, Manish, Hamil, R, Deans, Andrew, Furniss, J, Clohisey, S, Elliott, Anne, Solstice, A R, Deas, C, Tee, Caroline, Connell, David, Sutherland, Debbie, George, J, Mohammed, S, Bunker, Jenny, Holmes, Katie, Dipper, A, Morley, Anna, Arnold, David, Adamali, H, Welch, H, Morrison, Leigh, Stadon, Louise, Maskell, Nick, Barratt, Shaney, Dunn, Sarah, Waterson, Samuel, Jayaraman, Bhagy, Light, Tessa, Selby, N, Hosseini, A, Shaw, Karen, Almeida, Paula, Needham, Robert, Thomas, Andrew K, Matthews, Laura, Gupta, Ayushman, Nikolaidis, Athanasios, Dupont, Catherine, Bonnington, J, Chrystal, Melanie, Greenhaff, P L, Linford, S, Prosper, Sabrina, Jang, W, Alamoudi, Asma, Bloss, Angela, Megson, Clare, Nicoll, Debby, Fraser, Emily, Pacpaco, Edmund, Conneh, Florence, Ogg, G, McShane, H, Koychev, Ivan, Chen, Jin, Pimm, John, Ainsworth, Mark, Pavlides, M, Sharpe, M, Havinden-Williams, May, Petousi, Nayia, Talbot, Nick, Carter, Penny, Kurupati, Prathiba, Dong, T, Peng, Yanchun, Burns, A, Kanellakis, N, Korszun, A, Connolly, B, Busby, J, Peto, T, Patel, B, Nolan, C M, Cristiano, Daniele, Walsh, J A, Liyanage, Kamal, Gummadi, Mahitha, Dormand, N, Polgar, Oliver, George, P, Barker, R E, Patel, Suhani, Gibbons, M, Matila, Darwin, Jarvis, Hannah, Lim, Lai, Olaosebikan, Olaoluwa, Ahmad, Shanaz, Brill, Simon, Mandal, S, Laing, C, Michael, Alice, Reddy, A, Johnson, C, Baxendale, H, Parfrey, H, Mackie, J, Newman, J, Pack, Jamie, Parmar, J, Paques, K, Garner, Lucie, Harvey, Alice, Summersgill, C, Holgate, D, Hardy, E, Oxton, J, Pendlebury, Jessica, McMorrow, L, Mairs, N, Majeed, N, Dark, P, Ugwuoke, R, Knight, Sean, Whittaker, S, Strong-Sheldrake, Sophia, Matimba-Mupaya, Wadzanai, Chowienczyk, P, Pattenadk, Dibya, Hurditch, E, Chan, Flora, Carborn, H, Foot, H, Bagshaw, J, Hockridge, J, Sidebottom, J, Lee, Ju Hee, Birchall, K, Turner, Kim, Haslam, L, Holt, L, Milner, L, Begum, M, Marshall, M, Steele, N, Tinker, N, Ravencroft, Phillip, Butcher, Robyn, Misra, S, Coburn, Zach, Fairman, Alexandra, Ford, Amber, Holbourn, Ailsa, Howell, Alice, Lawrie, Allan, Lye, Alison, Mbuyisa, Angeline, Zawia, Amira, Holroyd-Hind, B, Thamu, B, Clark, Cameron, Jarman, Claire, Norman, C, Roddis, C, Foote, David, Lee, Elvina, Ilyas, F, Stephens, G, Newell, Helen, Turton, Helena, Macharia, Irene, Wilson, Imogen, Cole, Joby, McNeill, J, Meiring, J, Rodger, J, Watson, James, Chapman, Kerry, Harrington, Kate, Chetham, Luke, Hesselden, L, Nwafor, Lorenza, Dixon, Myles, Plowright, Megan, Wade, Phillip, Gregory, Rebecca, Lenagh, Rebecca, Stimpson, R, Megson, Sharon, Newman, Tom, Cheng, Yutung, Goodwin, Camelia, Heeley, Cheryl, Sissons, D, Sowter, D, Gregory, Heidi, Wynter, Inez, Hutchinson, John, Kirk, Jill, Bennett, Kaytie, Slack, Katie, Allsop, Lynne, Holloway, Leah, Flynn, Margaret, Gill, Mandy, Greatorex, M, Holmes, Megan, Buckley, Phil, Shelton, Sarah, Turner, Sarah, Sewell, Terri Ann, Whitworth, V, Lovegrove, Wayne, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Vickers, Carinna, Redwood, Dawn, Tilley, Jo, Palmer, Sue, Wainwright, Tania, Breen, G, Hotopf, M, Dunleavy, A, Teixeira, J, Ali, Mariam, Mencias, Mark, Msimanga, N, Siddique, Sulman, Samakomva, T, Tavoukjian, Vera, Forton, D, Ahmed, R, Cook, Amanda, Thaivalappil, Favas, Connor, Lynda, Rees, Tabitha, McNarry, M, Williams, N, McCormick, Jacqueline, McIntosh, Jerome, Vere, Joanne, Coulding, Martina, Kilroy, Susan, Turner, Victoria, Butt, Al-Tahoor, Savill, Heather, Fraile, Eva, Ugoji, Jacinta, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Osborne, Lynn, Chablani, Manish, Banerjee, A, David, A, Pakzad, A, Rangelov, B, Williams, B, Denneny, E, Willoughby, J, Xu, M, Mehta, P, Batterham, R, Bell, R, Aslani, S, Lilaonitkul, W, Checkley, A, Bang, Dongchun, Basire, Donna, Lomas, D, Wall, E, Plant, Hannah, Roy, K, Heightman, M, Lipman, M, Merida Morillas, Marta, Ahwireng, Nyarko, Chambers, R C, Jastrub, Roman, Logan, S, Hillman, T, Botkai, A, Casey, A, Neal, A, Newton-Cox, A, Cooper, B, Atkin, C, McGee, C, Welch, C, Wilson, D, Sapey, E, Qureshi, H, Hazeldine, J, Lord, J M, Nyaboko, J, Short, J, Stockley, J, Dasgin, J, Draxlbauer, K, Isaacs, K, Mcgee, K, Yip, K P, Ratcliffe, L, Bates, M, Ventura, M, Ahmad Haider, N, Gautam, N, Baggott, R, Holden, S, Madathil, S, Walder, S, Yasmin, S, Hiwot, T, Jackson, T, Soulsby, T, Kamwa, V, Peterkin, Z, Suleiman, Z, Chaudhuri, N, Wheeler, H, Djukanovic, R, Samuel, R, Sass, T, Wallis, T, Marshall, B, Childs, C, Marouzet, E, Harvey, M, Fletcher, S, Dickens, C, Beckett, P, Nanda, U, Daynes, E, Charalambou, A, Yousuf, A J, Lea, A, Prickett, A, Gooptu, Bibek, Hargadon, Beverley, Bourne, Charlotte, Christie, C, Edwardson, C, Lee, D, Baldry, E, Stringer, E, Woodhead, F, Mills, G, Arnold, H, Aung, H, Qureshi, I N, Finch, J, Skeemer, J, Hadley, K, Khunti, Kamlesh, Carr, Liesel, Ingram, L, Aljaroof, M, Bakali, M, Bakau, M, Baldwin, M, Bourne, Michelle, Pareek, Manish, Soares, M, Tobin, Martin, Armstrong, Natalie, Brunskill, Nigel, Goodman, N, Cairns, P, Haldar, Pranab, McCourt, P, Dowling, R, Russell, Richard, Diver, Sarah, Edwards, Sarah, Glover, Sarah, Parker, S, Siddiqui, Salman, Ward, T J C, Mcnally, T, Thornton, T, Yates, Tom, Ibrahim, W, Monteiro, Will, Thickett, D, Wilkinson, D, Broome, M, McArdle, P, Upthegrove, R, Wraith, D, Langenberg, C, Summers, C, Bullmore, E, Heeney, J L, Schwaeble, W, Sudlow, C L, Adeloye, D, Newby, D E, Rudan, I, Shankar-Hari, M, Thorpe, M, Pius, R, Walmsley, S, McGovern, A, Ballard, C, Allan, L, Dennis, J, Cavanagh, J, Petrie, J, O'Donnell, K, Spears, M, Sattar, N, MacDonald, S, Guthrie, E, Henderson, M, Guillen Guio, Beatriz, Zhao, Bang, Lawson, C, Overton, Charlotte, Taylor, Chris, Tong, C, Mukaetova-Ladinska, Elizabeta, Turner, E, Pearl, John E, Sargant, J, Wormleighton, J, Bingham, Michelle, Sharma, M, Steiner, Mike, Samani, Nilesh, Novotny, Petr, Free, Rob, Allen, R J, Finney, Selina, Terry, Sarah, Brugha, Terry, Plekhanova, Tatiana, McArdle, A, Vinson, B, Spencer, L G, Reynolds, W, Ashworth, M, Deakin, B, Chinoy, H, Abel, K, Harvie, M, Stanel, S, Rostron, A, Coleman, C, Baguley, D, Hufton, E, Khan, F, Hall, I, Stewart, I, Fabbri, L, Wright, L, Kitterick, P, Morriss, R, Johnson, S, Bates, A, Antoniades, C, Clark, D, Bhui, K, Channon, K M, Motohashi, K, Sigfrid, L, Husain, M, Webster, M, Fu, X, Li, X, Kingham, L, Klenerman, P, Miiler, K, Carson, G, Simons, G, Huneke, N, Calder, P C, Baldwin, D, Bain, S, Lasserson, D, Daines, L, Bright, E, Stern, M, Crisp, P, Dharmagunawardena, R, Reddington, A, Wight, A, Bailey, L, Ashish, A, Robinson, E, Cooper, J, Broadley, A, Turnbull, A, Brookes, C, Sarginson, C, Ionita, D, Redfearn, H, Elliott, K, Barman, L, Griffiths, L, Guy, Z, Gill, Rhyan, Nathu, Rashmita, Harris, Edward, Moss, P, Finnigan, J, Saunders, Kathryn, Saunders, Peter, Kon, S, Kon, Samantha S, O'Brien, Linda, Shah, K, Shah, P, Richardson, Emma, Brown, V, Brown, M, Brown, Jo, Brown, J, Brown, Ammani, Brown, Angela, Choudhury, N, Jones, S, Jones, H, Jones, L, Jones, I, Jones, G, Jones, Heather, Jones, Don, Davies, Ffyon, Davies, Ellie, Davies, Kim, Davies, Gareth, Davies, Gwyneth A, Howard, K, Porter, Julie, Rowland, J, Rowland, A, Scott, Kathryn, Singh, Suver, Singh, Claire, Thomas, S, Thomas, Caradog, Lewis, Victoria, Lewis, J, Lewis, D, Harrison, P, Francis, C, Francis, R, Hughes, Rachel Ann, Hughes, Joan, Hughes, A D, Thompson, T, Kelly, S, Smith, D, Smith, Nikki, Smith, Andrew, Smith, Jacqui, Smith, Laurie, Smith, Susan, Evans, Teriann, Evans, Ranuromanana I, Evans, D, Evans, R, Evans, H, and Evans, J

Published
2023
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7. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Author

Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage., Comment: This work is published at MLMIR 2018: Machine Learning for Medical Image Reconstruction

Published
2019
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8. 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

9. Improving the generalizability of convolutional neural network-based segmentation on CMR images

Author

Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., Comment: 15 pages, 8 figures

Published
2019
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10. Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Author

Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, Jos$é$ Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Matthews, Paul M., Rueckert, Daniel, and Glocker, Ben

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study., Comment: 14 pages, 7 figures, Journal of Cardiovascular Magnetic Resonance

Published
2019

11. High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

Author

Attar, Rahman, Pereanez, Marco, Gooya, Ali, Alba, Xenia, Zhang, Le, 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

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge,this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework., Comment: Accepted in STACOM workshop of MICCAI2018

Published
2019

12. 3-Dimensional Strain Analysis of Hypertrophic Cardiomyopathy: Insights From the NHLBI International HCM Registry

Author

Heydari, Bobak, Satriano, Alessandro, Jerosch-Herold, Michael, Kolm, Paul, Kim, Dong-Yun, Cheng, Kathleen, Choi, Yuna L., Antiochos, Panagiotis, White, James A., Mahmod, Masliza, Chan, Kenneth, Raman, Betty, Desai, Milind Y., Ho, Carolyn Y., Dolman, Sarahfaye F., Desvigne-Nickens, Patrice, Maron, Martin S., Friedrich, Matthias G., Schulz-Menger, Jeanette, Piechnik, Stefan K., Appelbaum, Evan, Weintraub, William S., Neubauer, Stefan, Kramer, Christopher M., and Kwong, Raymond Y.

Published
2023
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14. Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

Author

Zhang, Le, Gooya, Ali, Pereanez, Marco, Dong, Bo, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9\%/4.6\%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data., Comment: 12 pages, 5 figures, accepted by IEEE Transactions on Biomedical Engineering

Published
2018

15. Acute Response in the Noninfarcted Myocardium Predicts Long-Term Major Adverse Cardiac Events After STEMI

Author

Shanmuganathan, Mayooran, Masi, Ambra, Burrage, Matthew K., Kotronias, Rafail A., Borlotti, Alessandra, Scarsini, Roberto, Banerjee, Abhirup, Terentes-Printzios, Dimitrios, Zhang, Qiang, Hann, Evan, Tunnicliffe, Elizabeth, Lucking, Andrew, Langrish, Jeremy, Kharbanda, Rajesh, De Maria, Giovanni Luigi, Banning, Adrian P., Choudhury, Robin P., Channon, Keith M., Piechnik, Stefan K., and Ferreira, Vanessa M.

Published
2023
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16. Clinical Utility of Cardiovascular Magnetic Resonance Before Invasive Coronary Angiography in Suspected Non-ST-segment-Elevation Myocardial Infarction

Author

Shanmuganathan, Mayooran, primary, Nikolaidou, Chrysovalantou, additional, Burrage, Matthew K., additional, Borlotti, Alessandra, additional, Kotronias, Rafail, additional, Scarsini, Roberto, additional, Banerjee, Abhirup, additional, Terentes-Printzios, Dimitrios, additional, Pitcher, Alex, additional, Gara, Edit, additional, Langrish, Jeremy, additional, Lucking, Andrew, additional, Choudhury, Robin, additional, De Maria, Giovanni Luigi, additional, Banning, Adrian, additional, Piechnik, Stefan K., additional, Channon, Keith M., additional, and Ferreira, Vanessa M., additional

Published
2024
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17. Concurrent Left Ventricular Myocardial Diffuse Fibrosis and Left Atrial Dysfunction Strongly Predict Incident Heart Failure

Author

Wong, Mark Y.Z., primary, Vargas, Jose D., additional, Naderi, Hafiz, additional, Sanghvi, Mihir M., additional, Raisi-Estabragh, Zahra, additional, Suinesiaputra, Avan, additional, Bonazzola, Rodrigo, additional, Attar, Rahman, additional, Ravikumar, Nishant, additional, Hann, Evan, additional, Neubauer, Stefan, additional, Piechnik, Stefan K., additional, Frangi, Alejandro F., additional, Petersen, Steffen E., additional, and Aung, Nay, additional

Published
2024
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18. The Role of Coronary Blood Flow and Myocardial Edema in the Pathophysiology of Takotsubo Syndrome

Author

Couch, Liam S., primary, Thomas, Katharine E., additional, Marin, Federico, additional, Terentes-Printzios, Dimitrios, additional, Kotronias, Rafail A., additional, Chai, Jason, additional, Lukaschuk, Elena, additional, Shanmuganathan, Mayooran, additional, Kellman, Peter, additional, Langrish, Jeremy P., additional, Channon, Keith M., additional, Neubauer, Stefan, additional, Piechnik, Stefan K., additional, Ferreira, Vanessa M., additional, de Maria, Giovanni Luigi, additional, and Banning, Adrian P., additional

Published
2024
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20. Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Author

Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed., Comment: accepted by MICCAI 2018

Published
2018

22. Myocardial Involvement After Hospitalization for COVID-19 Complicated by Troponin Elevation: A Prospective, Multicenter, Observational Study

Author

Artico, Jessica, Shiwani, Hunain, Moon, James C., Gorecka, Miroslawa, McCann, Gerry P., Roditi, Giles, Morrow, Andrew, Mangion, Kenneth, Lukaschuk, Elena, Shanmuganathan, Mayooran, Miller, Christopher A., Chiribiri, Amedeo, Prasad, Sanjay K., Adam, Robert D., Singh, Trisha, Bucciarelli-Ducci, Chiara, Dawson, Dana, Knight, Daniel, Fontana, Marianna, Manisty, Charlotte, Treibel, Thomas A., Levelt, Eylem, Arnold, Ranjit, Macfarlane, Peter W., Young, Robin, McConnachie, Alex, Neubauer, Stefan, Piechnik, Stefan K., Davies, Rhodri H., Ferreira, Vanessa M., Dweck, Marc R., Berry, Colin, Greenwood, John P., Kelly, Bernard, Goreka, Miroslawa, Somers, Kathryn, Byrom-Goulthorp, Roo J., Anderson, Michelle, Britton, Laura, Richards, Fiona, Jones, Laura M., Moss, Alastair, Fisher, Jude, Wormleighton, Joanne, Parke, Kelly, Wright, Rachel, Yeo, Jian, Falconer, Judith, Harries, Valerie, Henderson, Paula, Newby, David, Popescu, Iulia, Zhang, Qiang, Raman, Betty, Channon, Keith, Krasopoulos, Catherine, Nunes, Claudia, Da Silva Rodrigues, Liliana, Nixon, Harriet, Panopoulou, Athanasia, Fletcher, Alison, Manley, Peter, Sykes, Robert, Fallon, Kirsty, Brown, Ammani, Kelly, Laura, McGinley, Christopher, Briscoe, Michael, Woodward, Rosemary, Hopkins, Tracey, McLennan, Evonne, Tynan, Nicola, Dymock, Laura, Swoboda, Peter, Wright, Judith, Exley, Donna, Steeds, Richard, Hutton, Kady, MacDonald, Sonia, Shetye, Abhishek, Orsborne, Christopher, Woodville-Jones, William, Ferguson, Susan, Bratis, Konstantinos, Fairbairn, Timothy, Sionas, Michail, Widdows, Peris, Gee Chew, Pei, Marsden, Christian, Collins, Tom, George, Linsha, Kearney, Lisa, Flett, Andrew, Smith, Simon, Zhenge, Alice, Harvey, Jake, Inacio, Liliana, Hanam-Penfold, Tomas, Gruner, Lucy, Razvi, Yousuf S.K., Crause, Jacolene, Davies, Nina M., Brown, James T., Chaco, Liza, Patel, Rishi, Kotecha, Tushar, Knight, Dan S., Green, Thomas, Ripley, David, Thompson, Maria, Cifra, Ugochi Akerele Elna, Alskaf, Ebraham, Crawley, Richard, Villa, Adriana, Nightingale, Angus K., Wright, Kim, Bonnick, Esther D., Hopkins, Emma, George, Jessy, Joseph, Linta, Cole, Graham, Vimalesvaran, Kavitha, Ali, Nadine, Carr, Caitlin R., Ross, Alexandra A.R., King, Clara, Farzad, Zohreh, Salmi, Sara A., Kirby, Kevin, McDiarmid, Adam, Stevenson, Hannah J., Matsvimbo, Pamela S., Joji, Lency, Fearby, Margaret, Brown, Benjamin, Bunce, Nicholas, Jennings, Robert, Sookhoo, Vennessa, Joshi, Shatabdi, Kanagala, Prathap, Fullalove, Sandra, Toohey, Catherine, Fenlon, Kate, Bellenger, Nicholas, He, Jingzhou, Statton, Sarah, Pamphilon, Nicola, Steele, Anna, Ball, Claire, McGahey, Ann, Balma, Silvia, Wilkes, Lynsey, Lewis, Katy, Walter, Michelle, Ionescu, Adrian, Ninan, Tishi, Richards, Suzanne, Williams, Marie, Alfakih, Khaled, Pilgrim, Samia, Joy, George, and Hussain, Ifza

Published
2023
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23. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Author

Bai, Wenjia, Sinclair, Matthew, Tarroni, Giacomo, Oktay, Ozan, Rajchl, Martin, Vaillant, Ghislain, Lee, Aaron M., Aung, Nay, Lukaschuk, Elena, Sanghvi, Mihir M., Zemrak, Filip, Fung, Kenneth, Paiva, Jose Miguel, Carapella, Valentina, Kim, Young Jin, Suzuki, Hideaki, Kainz, Bernhard, Matthews, Paul M., Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, Glocker, Ben, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images., Comment: Accepted for publication by Journal of Cardiovascular Magnetic Resonance

Published
2017

25. Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets

Author

Hann, Evan, Gonzales, Ricardo A., Popescu, Iulia A., Zhang, Qiang, Ferreira, Vanessa M., Piechnik, Stefan K., 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, Papież, Bartłomiej W., editor, Yaqub, Mohammad, editor, Jiao, Jianbo, editor, Namburete, Ana I. L., editor, and Noble, J. Alison, editor

Published
2021
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26. Symptom Persistence Despite Improvement in Cardiopulmonary Health – Insights from longitudinal CMR, CPET and lung function testing post-COVID-19

Author

Cassar, Mark Philip, Tunnicliffe, Elizabeth M., Petousi, Nayia, Lewandowski, Adam J., Xie, Cheng, Mahmod, Masliza, Samat, Azlan Helmy Abd, Evans, Rachael A., Brightling, Christopher E., Ho, Ling-Pei, Piechnik, Stefan K., Talbot, Nick P., Holdsworth, David, Ferreira, Vanessa M., Neubauer, Stefan, and Raman, Betty

Published
2021
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27. Predictors of Major Atrial Fibrillation Endpoints in the National Heart, Lung, and Blood Institute HCMR

Author

Kramer, Christopher M., DiMarco, John P., Kolm, Paul, Ho, Carolyn Y., Desai, Milind Y., Kwong, Raymond Y., Dolman, Sarahfaye F., Desvigne-Nickens, Patrice, Geller, Nancy, Kim, Dong-Yun, Maron, Martin S., Appelbaum, Evan, Jerosch-Herold, Michael, Friedrich, Matthias G., Schulz-Menger, Jeanette, Piechnik, Stefan K., Mahmod, Masliza, Jacoby, Daniel, White, James, Chiribiri, Amedeo, Helms, Adam, Choudhury, Lubna, Michels, Michelle, Bradlow, William, Salerno, Michael, Dawson, Dana K., Weinsaft, Jonathan W., Berry, Colin, Nagueh, Sherif F., Buccarelli-Ducci, Chiara, Owens, Anjali, Casadei, Barbara, Watkins, Hugh, Weintraub, William S., and Neubauer, Stefan

Published
2021
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29. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge

Author

Raman, Betty, Cassar, Mark Philip, Tunnicliffe, Elizabeth M., Filippini, Nicola, Griffanti, Ludovica, Alfaro-Almagro, Fidel, Okell, Thomas, Sheerin, Fintan, Xie, Cheng, Mahmod, Masliza, Mózes, Ferenc E., Lewandowski, Adam J., Ohuma, Eric O., Holdsworth, David, Lamlum, Hanan, Woodman, Myles J., Krasopoulos, Catherine, Mills, Rebecca, McConnell, Flora A. Kennedy, Wang, Chaoyue, Arthofer, Christoph, Lange, Frederik J., Andersson, Jesper, Jenkinson, Mark, Antoniades, Charalambos, Channon, Keith M., Shanmuganathan, Mayooran, Ferreira, Vanessa M., Piechnik, Stefan K., Klenerman, Paul, Brightling, Christopher, Talbot, Nick P., Petousi, Nayia, Rahman, Najib M., Ho, Ling-Pei, Saunders, Kate, Geddes, John R., Harrison, Paul J., Pattinson, Kyle, Rowland, Matthew J., Angus, Brian J., Gleeson, Fergus, Pavlides, Michael, Koychev, Ivan, Miller, Karla L., Mackay, Clare, Jezzard, Peter, Smith, Stephen M., and Neubauer, Stefan

Published
2021
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32. Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging

Author

Zhang, Le, Pereañez, Marco, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Frangi, Alejandro F., Kang, Sing Bing, Series Editor, Singh, Sameer, Founding Editor, Bischof, Horst, Advisory Editor, Bowden, Richard, Advisory Editor, Dickinson, Sven, Advisory Editor, Jia, Jiaya, Advisory Editor, Lee, Kyoung Mu, Advisory Editor, Sato, Yoichi, Advisory Editor, Schiele, Bernt, Advisory Editor, Sclaroff, Stan, Advisory Editor, Lu, Le, editor, Wang, Xiaosong, editor, Carneiro, Gustavo, editor, and Yang, Lin, editor

Published
2019
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33. Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

Author

Hann, Evan, Biasiolli, Luca, Zhang, Qiang, Popescu, Iulia A., Werys, Konrad, Lukaschuk, Elena, Carapella, Valentina, Paiva, Jose M., Aung, Nay, Rayner, Jennifer J., Fung, Kenneth, Puchta, Henrike, Sanghvi, Mihir M., Moon, Niall O., Thomas, Katharine E., Ferreira, Vanessa M., Petersen, Steffen E., Neubauer, Stefan, Piechnik, Stefan K., 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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34. 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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39. Distinct Subgroups in Hypertrophic Cardiomyopathy in the NHLBI HCM Registry

Author

Mahmod, Masliza, Jacoby, Daniel, White, James, Chiribiri, Amedeo, Helms, Adam, Choudhury, Lubna, Michels, Michelle, Bradlow, William, Salerno, Michael, Heitner, Stephen, Prasad, Sanjay, Mohiddin, Saidi, Swoboda, Peter, Mahrholdt, Heiko, Bucciarelli-Ducci, Chiara, Weinsaft, Jonathan, Kim, Han, McCann, Gerry, van Rossum, Albert, Williamson, Eric, Flett, Andrew, Dawson, Dana, Mongeon, F. Pierre, Olivotto, Iacopo, Crean, Andrew, Owens, Anjali, Anderson, Lisa, Biagini, Elena, Newby, David, Berry, Colin, Kim, Bette, Larose, Eric, Abraham, Theodore, Sherrid, Mark, Nagueh, Sherif, Rimoldi, Ornella, Elstein, Eleanor, Autore, Camillo, Neubauer, Stefan, Kolm, Paul, Ho, Carolyn Y., Kwong, Raymond Y., Desai, Milind Y., Dolman, Sarahfaye F., Appelbaum, Evan, Desvigne-Nickens, Patrice, DiMarco, John P., Friedrich, Matthias G., Geller, Nancy, Harper, Andrew R., Jarolim, Petr, Jerosch-Herold, Michael, Kim, Dong-Yun, Maron, Martin S., Schulz-Menger, Jeanette, Piechnik, Stefan K., Thomson, Kate, Zhang, Cheng, Watkins, Hugh, Weintraub, William S., and Kramer, Christopher M.

Published
2019
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42. Real-Time Prediction of Segmentation Quality

Author

Robinson, Robert, Oktay, Ozan, Bai, Wenjia, Valindria, Vanya V., Sanghvi, Mihir M., Aung, Nay, Paiva, José M., Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Kainz, Bernhard, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Rueckert, Daniel, Glocker, Ben, 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, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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43. Multi-Input and Dataset-Invariant Adversarial Learning (MDAL) for Left and Right-Ventricular Coverage Estimation in Cardiac MRI

Author

Zhang, Le, Pereañez, Marco, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Frangi, Alejandro F., 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, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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44. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Author

Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, 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, Knoll, Florian, editor, Maier, Andreas, editor, and Rueckert, Daniel, editor

Published
2018
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48. Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

Author

Thomas, Katharine E, Lukaschuk, Elena, Shanmuganathan, Mayooran, Kitt, Jamie A, Popescu, Iulia A, Neubauer, Stefan, Piechnik, Stefan K, and Ferreira, Vanessa M

Subjects
MYOCARDIUM physiology, REFERENCE values, AGE distribution, MAGNETIC resonance imaging, SIMULATION methods in education, SEX distribution, HEART beat, DESCRIPTIVE statistics, RESEARCH funding, DIAGNOSTIC errors
Abstract

Aims Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). Methods and results One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. Conclusion Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Author

Zhang, Qiang, Burrage, Matthew K., Lukaschuk, Elena, Shanmuganathan, Mayooran, Popescu, Iulia A., Nikolaidou, Chrysovalantou, Mills, Rebecca, Werys, Konrad, Hann, Evan, Barutcu, Ahmet, Polat, Suleyman D., Salerno, Michael, Jerosch-Herold, Michael, Kwong, Raymond Y., Watkins, Hugh C., Kramer, Christopher M., Neubauer, Stefan, Ferreira, Vanessa M., and Piechnik, Stefan K.

Published
2021
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50. Association between subclinical atherosclerosis and cardiac structure and function—results from the UK Biobank Study

Author

Simon, Judit, primary, Fung, Kenneth, additional, Raisi-Estabragh, Zahra, additional, Aung, Nay, additional, Khanji, Mohammed Y, additional, Zsarnóczay, Emese, additional, Merkely, Béla, additional, Munroe, Patricia B, additional, Harvey, Nicholas C, additional, Piechnik, Stefan K, additional, Neubauer, Stefan, additional, Leeson, Paul, additional, Petersen, Steffen E, additional, and Maurovich-Horvat, Pál, additional

Published
2023
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