A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
This addresses segmentation challenges in microbial single-cell analysis, offering a more efficient training approach, though it is incremental as it builds on existing detection and variational methods.
The paper tackles microbial cell segmentation by combining YOLOv5 detection with a variational segmentation using B-splines based on cell geometry, achieving performance on par with ML-based methods while requiring only bounding box training data instead of labor-intensive segmentation maps.
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.