A Hybrid Multi-Object Segmentation Framework with Model-Based B-Splines for Microbial Single Cell Analysis

IEEE International Symposium on Biomedical Imaging (ISBI), vol. 7, pp. 1–5

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.

Authors 3

1. Karina Ruzaeva Aachen

   RWTH Aachen University · Aachen Institute for Advanced Study in Computational Engineering Science

   Affiliation as printed

   RWTH Aachen University,AICES,Aachen,Germany

   AICES, RWTH Aachen University, Aachen, Germany

2. Katharina Nöh Aachen

   RWTH Aachen University · Aachen Institute for Advanced Study in Computational Engineering Science

   Affiliation as printed

   RWTH Aachen University,AICES,Aachen,Germany

   AICES, RWTH Aachen University, Aachen, Germany

3. Benjamin Berkels

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH,IBG-1: Biotechnology,Jülich,Germany