A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis

TL;DR

This paper introduces a hybrid microbial cell segmentation method combining ML detection with a B-spline based geometric segmentation, reducing training data requirements while maintaining high accuracy.

Contribution

It presents a novel hybrid segmentation framework that integrates ML detection with a model-based B-spline approach, requiring only bounding boxes for training.

Findings

Performs comparably to ML-based methods in segmentation accuracy.

Requires only bounding box annotations, reducing laborious training data collection.

Validated on time-lapse microscopy data of Corynebacterium glutamicum.

Abstract

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.