New Descriptor for Glomerulus Detection in Kidney Microscopy Image

TL;DR

This paper introduces Segmental HOG, a new adaptive descriptor and segmentation method that improves the automatic detection of glomeruli in kidney microscopy images, addressing size, shape, and staining variability challenges.

Contribution

The paper presents a novel Segmental HOG descriptor and segmentation technique that adaptively fits to images, enhancing glomeruli detection accuracy over traditional Rectangular HOG.

Findings

Segmental HOG outperforms Rectangular HOG in detection accuracy.

The method effectively handles size, shape, and staining variations.

High-quality segmentation results are achieved.

Abstract

Glomerulus detection is a key step in histopathological evaluation of microscopy images of kidneys. However, the task of automatic detection of glomeruli poses challenges due to the disparity in sizes and shapes of glomeruli in renal sections. Moreover, extensive variations of their intensities due to heterogeneity in immunohistochemistry staining are also encountered. Despite being widely recognized as a powerful descriptor for general object detection, the rectangular histogram of oriented gradients (Rectangular HOG) suffers from many false positives due to the aforementioned difficulties in the context of glomerulus detection. A new descriptor referred to as Segmental HOG is 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 to acquire robustness to deformations of 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 reveal that Segmental HOG achieves significant improvements in detection performance compared to Rectangular HOG. The proposed descriptor and method for glomeruli detection present promising results and is expected to be useful in pathological evaluation.