Recently, the use of circle representation has emerged as a method to improve the identification of spherical objects (such as glomeruli, cells, and nuclei) in medical imaging studies. In traditional bounding box-based object detection, combining results from multiple models improves accuracy, especially when real-time processing isn't crucial. Unfortunately, this widely adopted strategy is not readily available for combining circle representations. In this paper, we propose Weighted Circle Fusion (WCF), a simple approach for merging predictions from various circle detection models. Our method leverages confidence scores associated with each proposed bounding circle to generate averaged circles. Our method undergoes thorough evaluation on a proprietary dataset for glomerular detection in object detection within whole slide imaging (WSI). The findings reveal a performance gain of 5 %, respectively, compared to existing ensemble methods. Furthermore, the Weighted Circle Fusion technique not only improves the precision of object detection in medical images but also notably decreases false detections, presenting a promising direction for future research and application in pathological image analysis.

翻译：近年来，圆形表示法已成为提升医学影像研究中球形物体（如肾小球、细胞和细胞核）识别能力的一种方法。在传统的基于边界框的目标检测中，整合多个模型的结果能提高准确率，尤其是在实时处理非关键场景下。然而，这一广泛采用的策略目前尚无法直接用于合并圆形表示。本文提出加权圆融合（WCF），这是一种融合不同圆形检测模型预测结果的简单方法。我们的方法利用每个候选边界圆相关的置信度分数来生成平均圆。我们在全切片成像（WSI）目标检测中用于肾小球检测的专有数据集上对本方法进行了全面评估。结果表明，与现有集成方法相比，性能分别提升了5%。此外，加权圆融合技术不仅提高了医学图像中目标检测的精度，还显著减少了误检，为病理图像分析的未来研究和应用提供了有前景的方向。