Spatial statistical modelling of capillary non-perfusion in the retina

被引：0
作者：
Ian J. C. MacCormick
Yalin Zheng
Silvester Czanner
Yitian Zhao
Peter J. Diggle
Simon P. Harding
Gabriela Czanner
机构：
[1] Department of Eye & Vision Science,
[2] Institute of Ageing and Chronic Disease,undefined
[3] University of Liverpool,undefined
[4] Malawi-Liverpool Wellcome Trust Clinical Research Programme,undefined
[5] Queen Elizabeth Central Hospital,undefined
[6] Centre for Clinical Brain Sciences,undefined
[7] University of Edinburgh,undefined
[8] School of Computing,undefined
[9] Mathematics and Digital Technology,undefined
[10] Faculty of Science and Engineering,undefined
[11] Manchester Metropolitan University,undefined
[12] Cixi Institute of Biomedical Engineering,undefined
[13] Ningbo Institute of Industrial Technology,undefined
[14] Chinese Academy of Sciences,undefined
[15] CHICAS,undefined
[16] Lancaster Medical School,undefined
[17] Lancaster University,undefined
[18] St Paul’s Eye Unit,undefined
[19] Royal Liverpool University Hospital,undefined
[20] Department of Biostatistics,undefined
[21] Institute of Translational Medicine,undefined
[22] University of Liverpool,undefined
来源：
Scientific Reports | / 7卷
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摘要：
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. Here we propose 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.
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