CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation

TL;DR

This paper introduces CovHuSeg, a post-processing algorithm designed to improve kidney glomeruli segmentation by ensuring anatomically plausible shapes, significantly enhancing the accuracy of various deep learning models.

Contribution

The paper presents a novel CovHuSeg post-processing method tailored for ball-shaped anatomical structures, improving segmentation quality in kidney pathology images.

Findings

Increased segmentation accuracy across multiple models

Effective removal of shape anomalies like holes or irregularities

Applicable to ball-shaped medical image segmentation tasks

Abstract

Segmentation has long been essential in computer vision due to its numerous real-world applications. However, most traditional deep learning and machine learning models need help to capture geometric features such as size and convexity of the segmentation targets, resulting in suboptimal outcomes. To resolve this problem, we propose using a CovHuSeg algorithm to solve the problem of kidney glomeruli segmentation. This simple post-processing method is specified to adapt to the segmentation of ball-shaped anomalies, including the glomerulus. Unlike other post-processing methods, the CovHuSeg algorithm assures that the outcome mask does not have holes in it or comes in unusual shapes that are impossible to be the shape of a glomerulus. We illustrate the effectiveness of our method by experimenting with multiple deep-learning models in the context of segmentation on kidney pathology images. The results show that all models have increased accuracy when using the CovHuSeg algorithm.