用新后处理方法提升肾小球分割精度，避免空洞和畸形形状。
CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation
- 基于协方差几何约束的后处理算法，专为球形病灶设计。
- 在多个深度模型上均显著提升分割准确率，消除空洞与异常形状。
- 适合病理图像中球形结构分割，尤其适用于肾小球分析。
分割在计算机视觉中至关重要，广泛应用于实际场景。然而，传统深度学习与机器学习模型难以捕捉目标的几何特征（如大小、凸性），导致分割效果不佳。为此，我们提出 CovHuSeg 算法，用于解决肾小球分割问题。该方法为后处理技术，专为球形异常（如肾小球）设计。与现有后处理方法不同，CovHuSeg 能确保输出掩码无空洞，且形状符合肾小球真实形态。我们在多种深度学习模型上对肾病理图像的分割任务进行实验，结果表明，所有模型在使用 CovHuSeg 后分割准确率均显著提高。
原文摘要 · Abstract (English)
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.
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