Statistical and topological summaries aid disease detection for segmented retinal vascular images

IF 1.9 4区 医学 Q3 HEMATOLOGY Microcirculation Pub Date : 2023-01-12 DOI:10.1111/micc.12799
John T. Nardini, Charles W. J. Pugh, Helen M. Byrne
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引用次数: 2

Abstract

Objective

Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images.

Methods

We compute 13 separate descriptor vectors (5 statistical, 8 topological) to summarize the morphology of retinal vessel segmentation images and train support vector machines to predict each image's disease classification from the summary vectors. We assess the performance of each descriptor vector, using five-fold cross validation to estimate their accuracy. We apply these methods to four datasets that were assembled from four existing data repositories; three datasets contain segmented retinal vascular images from one of the repositories, whereas the fourth “All” dataset combines images from four repositories.

Results

Among the 13 total descriptor vectors considered, either a statistical Box-counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels. On the combined “All” dataset, the Box-counting vector outperforms all other descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets.

Conclusion

Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease and assessing their current limitations. These methods could be incorporated into automated disease assessment tools.

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统计和拓扑总结有助于疾病检测的分割视网膜血管图像
目的疾病并发症可改变血管网络形态,破坏组织功能。视网膜微血管疾病是通过视网膜图像的视觉检查来评估的,但当疾病表现出沉默的症状或患者不能参加面对面的会议时,这可能是具有挑战性的。我们研究了机器学习算法在检测微血管疾病时的性能,当对分割的视网膜血管图像进行统计和拓扑摘要训练时。方法计算13个独立的描述符向量(5个统计向量,8个拓扑向量)来总结视网膜血管分割图像的形态,并训练支持向量机从总结向量中预测每张图像的疾病分类。我们评估每个描述符向量的性能,使用五倍交叉验证来估计它们的准确性。我们将这些方法应用于从四个现有数据存储库中组装的四个数据集;三个数据集包含来自其中一个存储库的分割视网膜血管图像,而第四个“全部”数据集结合了来自四个存储库的图像。结果在13个描述子向量中,统计盒计数描述子向量和拓扑泛洪描述子向量的准确率最高。在组合的“All”数据集上,Box-counting向量优于所有其他描述符,包括拓扑泛洪向量,它对不同数据集之间注释风格的差异很敏感。我们的工作代表了建立最适合识别微血管疾病和评估其当前局限性的计算方法的第一步。这些方法可以纳入自动化疾病评估工具。
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来源期刊
Microcirculation
Microcirculation 医学-外周血管病
CiteScore
5.00
自引率
4.20%
发文量
43
审稿时长
6-12 weeks
期刊介绍: The journal features original contributions that are the result of investigations contributing significant new information relating to the vascular and lymphatic microcirculation addressed at the intact animal, organ, cellular, or molecular level. Papers describe applications of the methods of physiology, biophysics, bioengineering, genetics, cell biology, biochemistry, and molecular biology to problems in microcirculation. Microcirculation also publishes state-of-the-art reviews that address frontier areas or new advances in technology in the fields of microcirculatory disease and function. Specific areas of interest include: Angiogenesis, growth and remodeling; Transport and exchange of gasses and solutes; Rheology and biorheology; Endothelial cell biology and metabolism; Interactions between endothelium, smooth muscle, parenchymal cells, leukocytes and platelets; Regulation of vasomotor tone; and Microvascular structures, imaging and morphometry. Papers also describe innovations in experimental techniques and instrumentation for studying all aspects of microcirculatory structure and function.
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