Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology

被引：22

作者：

Ginley, Brandon ^{[1
]}

Tomaszewski, John E. ^{[1
,2
]}

Yacoub, Rabi ^{[3
]}

Chen, Feng ^{[4
]}

Sarder, Pinaki ^{[1
,5
]}

机构：

[1] University at Buffalo-The State University of New York, Departments of Pathology and Anatomical Sciences, 207 Farber Hall, 3435 Main Street Buffalo, New York,14214, United States

[2] University at Buffalo-The State University of New York, Departments of Biomedical Informatics, 207 Farber Hall, 3435 Main Street Buffalo, New York,14214, United States

[3] University at Buffalo-The State University of New York, Departments of Medicine-Nephrology, 207 Farber Hall, 3435 Main Street Buffalo, New York,14214, United States

[4] Washington University School of Medicine in Saint Louis, Department of Medicine-Renal Division, Campus Box 8126, St. Louis,MO,63110, United States

[5] University at Buffalo-The State University of New York, Departments of Biomedical Engineering, 207 Farber Hall, 3435 Main Street Buffalo, New York,14214, United States

来源：

Journal of Medical Imaging | 2017年 / 4卷 / 02期

关键词：

Biopsy - Blood - Pathology - Proteins - Structural analysis;

D O I：

10.1117/1.JMI.4.2.021102

中图分类号：

学科分类号：

摘要：

The glomerulus is the blood filtering unit of the kidney. Each human kidney contains ∼1million glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical F-Testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of 0.88/0.96 and accuracy of 0.92, on n=1000 glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-Time diagnoses and interventions for renal care. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).

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