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

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

Brandon Ginley et al. J Med Imaging (Bellingham). 2017 Apr.

Abstract

The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] 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 [Formula: see text]-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of [Formula: see text] and accuracy of 0.92, on [Formula: see text] 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.

Keywords: F-test; Gabor filtering; digital pathology; glomerulus; texture analysis.

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Figures

Fig. 1
Fig. 1

Estimation of glomerular locations from renal biopsies. (a) A renal biopsy mimic image, (b) the inverse grayscale intensity of a depicting higher signals in the nuclei locations, (c) Gaussian blurring of the high intensity nuclei in b, (d) approximated glomerular regions obtained from c, (e) singly extracted glomerulus, and (f) the above method detects 87% of the glomeruli that a manual examination discovers.

Fig. 2
Fig. 2

Computational pipeline for segmenting the glomerular boundary. (a) H&E stained glomerular tissue image, (b) grayscale version of the image shown in a, (c) Gaussian blurred image of b, (d) intensity image of the first principal component of the Gabor filter bank outputs using as input the image shown in c, and (e) K-means clustering was used to find final Gabor boundary. (f) F-testing examines the entire image for similarity with e, and outputs 0 or 1 for each pixel, (g) morphological noise removal for the image in f, (h, i) respective binary masks obtained from F-testing and Gabor filter bank were distance transformed, (j) intensity image of a spatial weighting intensity map obtained from a, (k) heatmap of an average of intensity images in h–j, and (l) final segmentation after thresholding is shown using dashed black line. The segmentation obtained from initial Gabor [see (e)] is shown using black. The dashed black boundary depicts improved detection of glomerulus.

Fig. 3
Fig. 3

Evaluation of parameter tuning. All scores reported precision versus accuracy: (a) amount of blurring with Gaussian filter, (b) spacing between Gabor filter orientations, (c) restriction on the maximum frequency size of the Gabor filter bank, (d) overall weighting of the spatial weighting map, and (e) value of the final threshold, which defines the glomerular boundary. Optimal parameter values are presented in Sec. 2.3.

Fig. 4
Fig. 4

Glomerular segmentation performance for five different stains. (a–e) Automatic segmentations of glomeruli stained by H&E, PAS, Gömöri’s trichrome, CR, and Jones silver, respectively. (f) Scatter of sensitivity versus specificity for all 1000 glomerular images. (g–k) Scatter of sensitivity versus specificity for individual stains. Overall, H&E showed the highest performance with the lowest variation between samples.

Fig. 5
Fig. 5

Segmentation of a disease glomerulus. (a) A healthy glomerulus and (b) glomerulus from mouse model of FSGS. Bowman’s space is marked with a yellow arrow, hyalinosis with a red-green arrow, lumen space with a black arrow, and automatic boundary with a black line.

Fig. 6
Fig. 6

Histogram representations of the performance of our method. (a) Distribution of F-measure for 1000 images, (b) distribution of precision for 1000 images, and (c) distribution of recall for 1000 images. Using similar performance metrics Kato et al. reported 90.1% of the glomeruli from a large sample had an F-measure of 0.8 or greater using their method. We found that 99.1% of glomeruli scored an F-measure of 0.8 or greater using our method.

Fig. 7
Fig. 7

Computational pipeline overview. (a) Glomerular location estimation outline. Biopsy sized images are recommended to be stain normalized; a free MATLAB® toolbox is available from the University of Warwick. Glomerular biopsy location estimation is extensively discussed in Ref. . Each candidate region is sent to the boundary estimation method. (b) Images are preprocessed, and an initial mask is segmented by Gabor textural segmentation. Next, the Gabor textural segmentation output is F-tested locally at every pixel. The results of these steps are distance transformed and averaged with a spatial weighting map. Thresholding the average of all three sources yields the final glomerular boundary.

Fig. 8
Fig. 8

Motivation of using Gabor analysis and F-testing. (a) Patch of intraglomerular region, (b) Fourier transform of a, (c) patch of extraglomerular region, (d) Fourier transform of c, (e) representative intensity histograms for 16 regions similar to a and c, respectively, and (f) sum of Fourier spectra coefficients for intra- and extraglomerular regions. Error-bars represent standard deviations of the sum metrics computed using data from four different mice.

Fig. 9
Fig. 9

Influence of stain normalization. (a) Performance of glomerular location estimation before stain normalization for an image with low contrast between glomerular nuclei and tubular nuclei. (b) Drastic improvement in detected glomeruli after being normalized to a well-stained image.

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