CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous
  Cell Populations
Mehdi Naouar Gabriel Kalweit Anusha Klett Yannick Vogt Paula Silvestrini Diana Laura Infante Ramirez Roland Mertelsmann Joschka Boedecker Maria Kalweit
In recent years, several unsupervised cell segmentation methods have been
presented, trying to omit the requirement of laborious pixel-level annotations
for the training of a cell segmentation model. Most if not all of these methods
handle the instance segmentation task by focusing on the detection of different
cell instances ignoring their type. While such models prove adequate for
certain tasks, like cell counting, other applications require the
identification of each cell's type. In this paper, we present CellMixer, an
innovative annotation-free approach for the semantic segmentation of
heterogeneous cell populations. Our augmentation-based method enables the
training of a segmentation model from image-level labels of homogeneous cell
populations. Our results show that CellMixer can achieve competitive
segmentation performance across multiple cell types and imaging modalities,
demonstrating the method's scalability and potential for broader applications
in medical imaging, cellular biology, and diagnostics.