28 February 2017 Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology
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Abstract
The glomerulus is the blood filtering unit of the kidney. Each human kidney contains 1    million 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)
Brandon Ginley, John E. Tomaszewski, Rabi Yacoub, Feng Chen, Pinaki Sarder, "Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology," Journal of Medical Imaging 4(2), 021102 (28 February 2017). https://doi.org/10.1117/1.JMI.4.2.021102 . Submission: Received: 1 August 2016; Accepted: 14 December 2016
Received: 1 August 2016; Accepted: 14 December 2016; Published: 28 February 2017
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