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Gabriela Czanner |
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Liverpool Group for Statistical Spatio-Temporal Modelling of Medical Images |
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The primary aim of research in the ophthalmic statistics group is the development of the novel methodology to design and analyses ophthalmic studies. Currently we are addressing a number of key challenges in inference and prediction from observational studies: 1. Spatial statistical models clinical images toward
understanding of ophthalmic diseases.
Manual grading of lesions in retinal images is relevant to clinical
management and clinical trials, but it is time-consuming and expensive.
Furthermore, it collects only limited information - such as lesion size or
frequency. The spatial distribution of lesions is ignored, even though it may
contribute to the overall clinical assessment of disease severity, and
correspond to microvascular and physiological topography. Capillary
non-perfusion (CNP) lesions are central to the pathogenesis of major causes
of vision loss. With clinicians Ian MacCormick and Simon Harding and imaging
specialists Yalin Zheng, Silvester Czanner we proposed a novel method to
analyse CNP using spatial statistical modelling. This quantifies the
percentage of CNP-pixels in each of 48 sectors and then characterises the
spatial distribution with goniometric functions. We applied our spatial
approach to a set of images from patients with malarial retinopathy, and
found it compares favourably with the raw percentage of CNP-pixels and also
with manual grading. Furthermore, we were able to quantify a biological
characteristic of macular CNP in malaria that had previously only been
described subjectively: clustering at the temporal raphe. Microvascular
location is likely to be biologically relevant to many diseases, and so our
spatial approach may be applicable to a diverse range of pathological
features in the retina and other organs. This work is now being extended in
our group into spatio-temporal models. 2. Longitudinal and spatio-temporal models for early detection, screening and monitoring of diseases. We recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers (Hughes et al, 2016). The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. Currently, a PhD student in my group, Wenyue Zhu, is extending the model to account for longitudinally collected images, in collaboration with Prof Simon Harding, imaging specialists Yalin Zheng and statistician Ruwanthi Kolamunnage-Dona. My student Mohamed Barkat is looking into proposing dynamic monitoring for endovascular surgeries. 3. Signal-to-noise ratio metrics for evaluation of the signal in the complex data and for feature extraction.
Neurons represent both signal and noise in binary electrical discharges termed action potentials. Hence, the standard signal-to-noise ratio (SNR) definition of signal amplitude squared and divided by the noise variance does not apply. We show that the SNR estimates a ratio of expected prediction errors. Using point process generalized linear models, we extend the standard definition to one appropriate for single neurons. In analyses of four neural systems, we show that single neuron SNRs range from −29 dB to −3 dB and that spiking history is often a more informative predictor of spiking propensity than the signal or stimulus activating the neuron. By generalizing the standard SNR metric, we make explicit the well-known fact that individual neurons are highly noisy information transmitters. PhD student Padraic Walsh, in my group, has been developing a new SNR metric that can be used for the selection of best set of predictive biomarkers from clinical observational data, especially when data are coming from varying modalities (e.g. image, genetics, physiology). 4.
Human,
statistical and machine learning for disease understanding from complex data.
We look into
comparison of human, statistical and machine approaches to complex datasets;
validation of generalisability and application to problems from medicine such
as glaucoma, keratoconus and gait analyses. This is
supported by EPSRC GCRF (PI: Yalin Zheng), EPSRC Mathematics Healthcare
Centre (PI: Ke Chen). The findings of our research contributes to ophthalmic research in the following area: diabetes related diseases, age-related diseases and malaria related diseases. |
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Members
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Dr Gabriela Czanner |
Lecturer in Ophthalmic Biostatistics and the leader of the group. |
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Ms Wenyue Zhu |
PhD student at Institute of Ageing and Chronic Diseases, working on spatio-temporal modelling of ophthalmic images (Since
2018) |
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Mr Mohamed Barkat |
MD student at Institute of Ageing and Chronic Diseases, working on
estimating the need for endovascular surgery (Since 2016) |
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Mr Joshua Bridge |
MRes student working on temporal modelling (2018) |
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Dr Joe Butler (former member) |
Dr Butler is a Biostatistics PDRA working on statistical models for prediction of conversion to wet age-related macular degeneration (project EDiMAD, Sept 2016-Feb 2017). |
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Dr Ian MacCormick |
Dr MacCormick is associated with Department of Eye and Vision Science. He collaborates with Gabriela on statistical methods for evaluation of surrogate markers and on spatial analysis of retinal images. |
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Mrs Riham El Saeiti |
Ms Riham El Saeiti is a PhD student at Department of Biostatistics. She is working on discriminant function analysis methods, mixed effect models and applications in medicine. Co-advised with Marta Garcia-Finana. Start 2013. |
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Ms Nazatulshima Hassan |
Ms Nazatulshima Hassan is a PhD student at Department of Biostatistics. She is working on variable selection for discriminant analysis for nominal data and logistic regression: application to SNP data. Co-advised with Marta Garcia-Finana and Andrea Jorgensen. Start 2014. |
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Mr Padraic Walsh (former
member) |
Mr Padraic Walsh is a Phd student at Department of Biostatistics. He is working on variable selection for discrimination for complex health care data, signal-to-noise ratio, quadratic discriminant analysis: application to diabetic retinopathy and imaging. Co-advised with Simon Harding and Marta Garcia-Finana. Start Oct 2013. |
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Ms Henal Desai (former member) |
Ms Henal Desai is a MRes Clinical Sciences student at IACD. She is working on developing a statistical model for predicting technical complications following endovascular surgery for aortic aneurysms, together with Gabriela, Mr Iain Roy (Vascular Research Fellow) and Professor S R Vallabhaneni (Professor of Vascular Surgery and Consultant Vascular & Endovascular Surgeon) from Liverpool Vascular & Endovascular Service. March-July 2016 Henal won the IACD MREs Best Poster Price for her project! |
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Chris Bell, Manoharan Kumararaj and Thomas Hughes (former member) |
Role of HbA1c, Cholesterol and Blood pressure in progression to sight threatening diabetic retinopathy. Clinical Research Students, 3rd year, Sept 2015-April 2016. |
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Ashwin Balu (3rd year clinical student) |
Role of HbA1c in diabetic retinopathy. Sept 2014-April 2015. |
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Amanda Waleh-Astrom (Mres student) |
Ordinal logistic regression and application in diabetic retinopathy. Sept-Dec 2014. |
Our
research have been funded by the following sources
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EPSRC MRC |
EPSRC GCRF grant (PI: Dr Yalin Zheng) University of Liverpool Centre for Mathematical Sciences in Healthcare https://news.liverpool.ac.uk/2015/12/16/57824/ Early prediction of diabetic retinopathy disease progression using longitudinal data and mixed effects models and discriminant analysis. Collaborators: Dr Marta Garcia-Finana (PI, UoL), Dr Gabriela Czanner (Co-I), Dr Trevor Cox, Prof Simon Harding (UoL), Prof Tony Marson, Dr Laura Bonnett |
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Dunhill Medical
Trust |
Early detection in macular disease: A psychophysical screening test
for neovascular age-related macular degeneration. Collaborators:
Dr. Paul Knox (PI), Dr Gabriela Czanner (Co-I),
Prof Heinrich Heimann, Prof Simon Harding |
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Wellcome Trust ViP
Fellowship in Medical Science |
Statistical
modelling for neural spikes. Collaborators:
Prof Emery Brown (Harvard/MIT) |
Selected research
papers of the group
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1.
Barrera V, MacCormick IJC, Czanner
G, et al., Neurovascular sequestration in paediatric P.falciparum
malaria is visible clinically in the retina. eLife
2018;7:e32208 DOI: 10.7554/eLife.32208 (IF 7.7) 2.
MacCormick IJC, Zheng Y, Czanner S, Zhao Y, Diggle PJ, Harding
SP and Czanner G. Spatial
statistical modelling of capillary non-perfusion in the retina. Scientific
Reports 7, 2017 https://www.nature.com/articles/s41598-017-16620-x
3.
Sahni J, Czanner G, Gutu T, Kate M Bennett K, Wuerger SM, Grierson I,
Murray-Dunning C, Holland MN, Harding
SP, Safety and acceptability of an organic light emitting diode sleep mask as
a potential therapy for retinal disease. Eye
2017; 31(1):97-106. PMCID: PMC5233944
(IF 2.1) 4.
Ho WMV, Hussain R, Czanner
G, Heimann H, Damato BE. Porous vs non-porous
orbital implants after enucleation for uveal melanoma: a randomized study. Ophthal Plast Reconstr Surg. 2016
[Epub ahead of print] PMID: 27861329 (IF 0.99) 5.
Silvester
A, Neal T, Czanner G, Briggs M,
Harding S, Kaye S. Adult bacterial conjunctivitis:
resistance patterns over 12 years in patients attending a large primary eye
care centre in the UK. BMJ Open Ophthalmol, 1(1) DOI:
10.1136/bmjophth-2016-000006 (IF 3.04) 6.
Aslam
TM, Tahir HJ, Parry NRA, Murray IJ, Kwak K, Heyes
R, Saleh MM, Czanner G, Ashworth
J. Automated measurement of visual acuity in pediatric
ophthalmic patients using principles of game design and tablet computers. Am J Ophthalmol.
2016(170):223-227. (IF 3.8) 7.
Hughes
D, Komarek A, Czanner
G, Garcia-Finana M. Dynamic longitudinal discriminant analysis using
multiple longitudinal markers. Stat
Methods Med Res. First published date: October 2016. DOI: 10.1177/0962280216674496 (IF 4.6) 8.
Knox
PK, MacCormick IJC, Mbale E, Malewa
M, Czanner G, Harding SP.
Longitudinal visuomotor development in a malaria
endemic area: cerebral malaria and beyond. PLoS One . 2016;11(10):e0164885.
doi:10.1371/journal.pone.0164885. PMCID: PMC5072745 (IF 3.2) 9.
Chan
YK, Czanner G, Shum HC, Williams
RL, Cheung N, Wong D. Towards better characterisation and quantification of
emulsification of silicone oil in vitro. Acta Ophthalmol 2016. [Epub
ahead of print] DOI:
10.1111/aos.13258 (IF 3.0) 10.
Vallabh NA, Sahni
JN, Parkes CK, Czanner G, Heimann
H, Damato B. Near infrared reflectance and autofluorescence imaging characteristics of choroidal
nevi. Eye 2016. DOI:
10.1038/eye.2016.183. (IF 2.1) 11.
Aslam TM, Parry NRA, Murray IJ, Salleh
M, Dal Col C, Mirza N, Czanner G,
Tahir HJ. Development and testing of an automated computer tablet-based
method for self-testing of high and low contrast near visual acuity in
ophthalmic patients. Graefes Arch Clin Exp Ophthalmol. 2016;
254(5):891-899. (IF 1.9) 12.
Mittal
R, Araujo I, Czanner G, Coupland
S. Perforin expression in eyelid sebaceous
carcinomas: a useful and specific immunomarker for
the differential diagnosis of eyelid carcinomas. Acta Ophthalmol 2016; 94(5):e325-e330. (IF
3.0) 13.
Cox T, Czanner G. A
practical divergence measure for survival distributions that can be estimated
from Kaplan-Meier curves. Stat Med
2016; 35(14):2406-2421. (IF 2.0) 14. Tidbury L, Czanner G, Newsham D. Fiat Lux: The
effect of illuminance on visual and stereo acuity testing, Graefes Arch Clin Exp Ophthalmol 2016; 254(6):1091-1097. (IF 1.9) 15.
Balaskas K, Tiew S, Czanner G, Tan AL,
Ashworth J, Biswas S, Aslam T. The novel evidenced assessment of tortuosity
system: interobserver reliability and agreement
with clinical assessment. Acta Ophthalmol Online 21 Dec 2015. DOI: 10.1111/aos.12907. (IF 3.0) 16.
MacCormick IJC, Czanner G,
Faragher B, Developing retinal biomarkers of
neurological disease – an analytical perspective. Biomark.
Med. 2015; 9(7): 691–701.
(IF 2.2) 17.
Boonarpha N, Zheng Y, Stangos AN, Lu
H, Raj A, Czanner G, Harding SP,
Nair-Sahni J, Standardisation of choroidal
thickness measurements using enhanced depth imaging optical coherence
tomography, Int J Ophthalmol
2015 18;8(3):484-91. (IF 0.9) 18.
Czanner G, Sarma SV, Ba D, Eden UT, Wu
W, Eskandar E, Lim HH, Temereanca
S, Suzuki WA and Brown EN. Measuring the signal-to-noise ratio of a neuron. Proc Natl Acad Sci U
S A. 2015 Jun 9;112(23):7141-6. (IF 9.4) 19.
Stewart RMK, Sheridan C.M, Hiscott PS, Czanner G, Kaye SB. Human conjunctival stem cells are
predominantly located in the medial canthal and
inferior forniceal areas. Invest Ophthalmol Vis Sci. 2015 Feb
26;56(3):2021-30. (IF 3.5) 20. Spiteri N, Siradas G, Czanner G, Batterbury
M, Kaye SB. Assessing the quality of ophthalmic anaesthesia. J Clin Anesth 2015 June; 27(4):285-9. (IF 1.3) 21. Cumberland PM, Czanner G, Bunce C, Dore CJ, Freemantle N, Garcia-Finana M.
Ophthalmic statistics note: the perils of dichotomizing continuous variables.
Br J Ophthalmol
2014;98: 841-843. (IF 3.0) 22. Raj A, Sahni JN,
Campa C, Czanner
G, Hagan R, Harding SP. Macular function: better than visual acuity for
assessing diabetic maculopathy? Eur J Ophthalmol 2013; 23(3): 459-460. (IF 1.0) 23. Green J, Czanner G, Reeves G , Watson J, Wise L, Roddam A,
Beral V. Menopausal hormone therapy
and risk of gastrointestinal cancer: prospective study and meta-analyses. Int J Cancer
2012;130(10): 2387-96. (IF 5.5) 24. Sarma S, Nguyen D, Czanner G, Wirth S, Suzuki W, Wilson MA, Brown E. Error analysis
for point process adaptive filters: A bootstrap approach. Neural Comput
2011;23(11): 2731-45. (IF1.6) 25. Petrasova A, Czanner S, Czanner G, Farrer J, Chalmers A, Wolke D. The influence of dynamic scene changes in positioning and
manipulating VR tasks. J Appl Math 2010;3(1):
259-268. (IF 1.5) 26. Green J, Czanner G, Reeves G, Watson J, Wise L, Beral V. Oral
bisphosphonates and risk of cancer of the oesophagus, stomach and colorectum: nested case-control study. Br Med J 2010;341(7772):
545-552. (IF 10.0) 27. Czanner
G, Eden UT, Wirth S, Yanike M, Suzuki WA, Brown EN. Analysis of between-trial
and within-trial neural spiking dynamics. J
Neurophysiol 2008;99(5): 2672-2693. (IF
4.6) 28.
Czanner G, Grün
S, Iyengar S. Theory of the snowflake plot and its relations
to higher-order analysis methods. Neural Comput 2005;17(7):
1456-1479 (IF 1.6) 29.
Krimer LS, Zaitsev AV,
Czanner G, Kroner S, Gonzalez-Burgos G, Povysheva
NV, Iyengar S, Barrionuevo
G, Lewis DA. Cluster
analysis-based physiological classification and morphological properties of
inhibitory neurons in layers 2-3 of monkey dorsolateral prefrontal cortex. J
Neurophysiol 2005;94(5): 3009-3022 (IF 2.9) |
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Contact: czanner@liverpool.ac.uk
Address: University of Liverpool, William Duncan Building, Room 287, Liverpool, L69 3GA
Last updated: 15 May 2018