  Tissue segmentation is the mainstay of pathological examination, whereas the
manual delineation is unduly burdensome. To assist this time-consuming and
subjective manual step, researchers have devised methods to automatically
segment structures in pathological images. Recently, automated machine and deep
learning based methods dominate tissue segmentation research studies. However,
most machine and deep learning based approaches are supervised and developed
using a large number of training samples, in which the pixelwise annotations
are expensive and sometimes can be impossible to obtain. This paper introduces
a novel unsupervised learning paradigm by integrating an end-to-end deep
mixture model with a constrained indicator to acquire accurate semantic tissue
segmentation. This constraint aims to centralise the components of deep mixture
models during the calculation of the optimisation function. In so doing, the
redundant or empty class issues, which are common in current unsupervised
learning methods, can be greatly reduced. By validation on both public and
in-house datasets, the proposed deep constrained Gaussian network achieves
significantly (Wilcoxon signed-rank test) better performance (with the average
Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with
improved stability and robustness, compared to other existing unsupervised
segmentation approaches. Furthermore, the proposed method presents a similar
performance (p-value > 0.05) compared to the fully supervised U-Net.
