论文标题
与基于模型的B型平台的混合多物体分割框架用于微生物单细胞分析
A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
论文作者
论文摘要
在本文中，我们提出了一种用于多物体微生物细胞分割的混合方法。该方法将基于ML的检测与基于几何学变化的分割结合了基于细胞形状的几何模型进行参数化的B-Spline。首先使用Yolov5进行检测。在第二步中，每个检测到的单元格被单独分段。因此，分割仅需要按每个基础进行，这使其适合于将几何学的先验知识结合的变异方法。在这里，分割的轮廓建模为闭合均匀的立方B型序列，其控制点使用已知的细胞几何形状进行参数化。与纯粹基于ML的细分方法相比，需要准确的分割图作为非常努力生产的训练数据，我们的方法只需要边界框即可作为培训数据。尽管如此，提出的方法仍以基于ML的分割方法在这种情况下使用。我们研究了谷氨酸杆菌的延时显微镜数据的拟议方法的性能。
In this paper, we propose a hybrid approach for multi-object microbial cell segmentation. The approach combines an ML-based detection with a geometry-aware variational-based segmentation using B-splines that are parametrized based on a geometric model of the cell shape. The detection is done first using YOLOv5. In a second step, each detected cell is segmented individually. Thus, the segmentation only needs to be done on a per-cell basis, which makes it amenable to a variational approach that incorporates prior knowledge on the geometry. Here, the contour of the segmentation is modelled as closed uniform cubic B-spline, whose control points are parametrized using the known cell geometry. Compared to purely ML-based segmentation approaches, which need accurate segmentation maps as training data that are very laborious to produce, our method just needs bounding boxes as training data. Still, the proposed method performs on par with ML-based segmentation approaches usually used in this context. We study the performance of the proposed method on time-lapse microscopy data of Corynebacterium glutamicum.
