In the recent era of AI, instance segmentation has significantly advanced boundary and object detection especially in diverse fields (e.g., biological and environmental research). Despite its progress, edge detection amid adjacent objects (e.g., organism cells) still remains intractable. This is because homogeneous and heterogeneous objects are prone to being mingled in a single image. To cope with this challenge, we propose the weighted Mask R-CNN designed to effectively separate overlapped objects in virtue of extra weights to adjacent boundaries. For numerical study, a range of experiments are performed with applications to simulated data and real data (e.g., Microcystis, one of the most common algae genera and cell membrane images). It is noticeable that the weighted Mask R-CNN outperforms the standard Mask R-CNN, given that the analytic experiments show on average 92.5% of precision and 96.4% of recall in algae data and 94.5% of precision and 98.6% of recall in cell membrane data. Consequently, we found that a majority of sample boundaries in real and simulated data are precisely segmented in the midst of object mixtures.

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加权蒙版R-CNN用于改善相邻边界分割

在AI的近代时代，实例分割已大大提高了边界和对象检测的效率，尤其是在各个领域（例如，生物学和环境研究）。尽管取得了进展，但是在相邻物体（例如生物细胞）之间的边缘检测仍然难以解决。这是因为同质和异质对象易于混合在单个图像中。为了应对这一挑战，我们提出了加权蒙版R-CNN，该加权蒙版R-CNN设计为借助对相邻边界的额外权重有效地分离重叠的对象。对于数值研究，进行了一系列实验，并将其应用于模拟数据和真实数据（例如微囊藻，是最常见的藻类和细胞膜图像之一）。值得注意的是，加权分析Mask R-CNN优于标准Mask R-CNN，因为分析实验显示，藻类数据的平均准确度为92.5％，召回率为96.4％，单元格中的准确度为94.5％，召回率为98.6％膜数据。因此，我们发现真实和模拟数据中的大多数样本边界在对象混合物中间被精确地分割了。