  Automated cell segmentation has become increasingly crucial for disease
diagnosis and drug discovery, as manual delineation is excessively laborious
and subjective. To address this issue with limited manual annotation,
researchers have developed semi/unsupervised segmentation approaches. Among
these approaches, the Deep Gaussian mixture model plays a vital role due to its
capacity to facilitate complex data distributions. However, these models assume
that the data follows symmetric normal distributions, which is inapplicable for
data that is asymmetrically distributed. These models also obstacles weak
generalization capacity and are sensitive to outliers. To address these issues,
this paper presents a novel asymmetric mixture model for unsupervised cell
segmentation. This asymmetric mixture model is built by aggregating certain
multivariate Gaussian mixture models with log-likelihood and
self-supervised-based optimization functions. The proposed asymmetric mixture
model outperforms (nearly 2-30% gain in dice coefficient, p<0.05) the existing
state-of-the-art unsupervised models on cell segmentation including the segment
anything.
