CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation
This addresses segmentation challenges for kidney pathology analysis, but it is incremental as it builds on existing methods with a specific post-processing step.
The paper tackled the problem of kidney glomeruli segmentation by proposing the CovHuSeg algorithm, a post-processing method that ensures masks are hole-free and shape-appropriate, resulting in increased accuracy for all tested deep-learning models.
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.