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

Abstract

BackgroundThe 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.ResultsA 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.ConclusionThe proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

Glomerulus Classification and Detection Based on Convolutional Neural Networks

“…The main problem of these techniques is the fact that R-HOG has rigid block division that results into considerable number of false positives. In order to improve the R-HOG framework, Kato et al [10] suggest the Segmental HOG (S-HOG) as potential candidate descriptor for Glomerulus detection, where the block division is not rigid, and uses nine discretized oriented gradients and Support Vector Machine (SVM [11]) as a supervised learning classifier. In [12], an analysis of renal microscopic images is applied by using a detection of borders improved with the Convex Hull algorithm ( [13]).…”

Measurement of glomerulus diameter and Bowman's space width of renal albino rats

“…Recently, in [16] a novel descriptor known as a segmental histogram of oriented gradients (HOG) was introduced to perform comprehensively the glomerulus detection in images of entire kidney sections. It utilized flexible blocks that can be adaptively fitted to the input images, which achieved robustness to the glomeruli deformations.…”

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

“…The remainder describe differences in descriptors in glomeruli from normal and pathologic populations, rather than isolating and labeling different glomeruli within the same wide image field. The current study [27] WSI, anti-desmin, fixed rat segmentation (normal, diabetic) [28] field, anti-nestin, fixed rat segmentation, characterization (normal, renal failure) [23] WSI, PAS, fixed mouse segmentation (normal) [25] WSI, Jones H&E/PAS/other, fixed human segmentation (normal) [22] WSI, H&E/PAS/others human, mouse, rat segmentation, characterization (normal, diabetic) [29] WSI, H&E primate segmentation, characterization (normal, diseased) [30] WSI, Masson's trichrome human segmentation - [31] field, H&E mouse segmentation - [24] patch, PAS human segmentation - [32] patch rat segmentation, characterization (normal, hypertrophy) [33] patch, H&E/PAS, fixed human segmentation, characterization (normal, proliferating) [34], [35] patch, H&E/PAS, fixed mouse, rat segmentation, characterization (normal) [36]- [38] patch -segmentation demonstrates the novel use of CNNs applied to frozen H&E sections to detect non-sclerotic and sclerotic glomeruli to assist pathologists in intra-operative interpretation of percent global glomerulosclerosis.…”

“…Translating detection techniques to whole-slide images (WSI) containing numerous glomeruli is a necessary but more difficult undertaking. The task of detection over large image regions can be facilitated using immunohistochemical stains such as nestin [28] and desmin [26], [27] to highlight glomerular podocytes and enhance the utility of segmentation algorithms. However, the immunohistological approach is less applicable for evaluation of preimplantation biopsies.…”