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Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image.

Kato T, Relator R, Ngouv H, Hirohashi Y, Takaki O, Kakimoto T, Okada K - BMC Bioinformatics (2015)

Bottom Line: Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution.Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan. katotsu@cs.gunma-u.ac.jp.

ABSTRACT

Background: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.

Results: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.

Conclusion: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

No MeSH data available.


Related in: MedlinePlus

Segmentation performances. The number of glomeruli are tallied to make a histogram with the F-measure, Precision, and Recall of the pixels on the basis of comparison of true segmentation with estimated segmentation on the x-axes. (See ‘Segmentation performance’ subsection for details)
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Fig8: Segmentation performances. The number of glomeruli are tallied to make a histogram with the F-measure, Precision, and Recall of the pixels on the basis of comparison of true segmentation with estimated segmentation on the x-axes. (See ‘Segmentation performance’ subsection for details)

Mentions: Herein, we discuss the performance of the segmentation algorithm. While the main purpose of the proposed method is detection, the proposed DCDP algorithm used for obtaining estimated segmentations may also be applied in some way in studies needing subsequent pathological evaluation [11]. To quantify the accuracy of the estimated areas within the predicted boundaries, 993 annotated glomeruli in Set B were used. True positive area (TPA), false positive area (FPA), and false negative area (FNA) were defined as follows: TPA is the intersection of the true area and estimated area; FPA is the relative complement of the true area in the estimated area; and FNA is the relative complement of the estimated area in the true area. For each glomerulus and its estimated area, F-measure, Precision, and Recall can be obtained by counting the pixels in the TPA, FPA, and FNA. The histograms of the F-measure, Precision, and Recall are plotted in Fig. 8, where the frequency is normalized so that the integral is one. Among the glomeruli, 90.1 % are estimated to have F-measures more than 0.8, ensuring reliable assessment of the medicinal effect for drug development.Fig. 8


Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image.

Kato T, Relator R, Ngouv H, Hirohashi Y, Takaki O, Kakimoto T, Okada K - BMC Bioinformatics (2015)

Segmentation performances. The number of glomeruli are tallied to make a histogram with the F-measure, Precision, and Recall of the pixels on the basis of comparison of true segmentation with estimated segmentation on the x-axes. (See ‘Segmentation performance’ subsection for details)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4590714&req=5

Fig8: Segmentation performances. The number of glomeruli are tallied to make a histogram with the F-measure, Precision, and Recall of the pixels on the basis of comparison of true segmentation with estimated segmentation on the x-axes. (See ‘Segmentation performance’ subsection for details)
Mentions: Herein, we discuss the performance of the segmentation algorithm. While the main purpose of the proposed method is detection, the proposed DCDP algorithm used for obtaining estimated segmentations may also be applied in some way in studies needing subsequent pathological evaluation [11]. To quantify the accuracy of the estimated areas within the predicted boundaries, 993 annotated glomeruli in Set B were used. True positive area (TPA), false positive area (FPA), and false negative area (FNA) were defined as follows: TPA is the intersection of the true area and estimated area; FPA is the relative complement of the true area in the estimated area; and FNA is the relative complement of the estimated area in the true area. For each glomerulus and its estimated area, F-measure, Precision, and Recall can be obtained by counting the pixels in the TPA, FPA, and FNA. The histograms of the F-measure, Precision, and Recall are plotted in Fig. 8, where the frequency is normalized so that the integral is one. Among the glomeruli, 90.1 % are estimated to have F-measures more than 0.8, ensuring reliable assessment of the medicinal effect for drug development.Fig. 8

Bottom Line: Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution.Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan. katotsu@cs.gunma-u.ac.jp.

ABSTRACT

Background: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.

Results: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.

Conclusion: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

No MeSH data available.


Related in: MedlinePlus