  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.
