  The ability of three-dimensional (3D) spheroid modeling to study the invasive
behavior of breast cancer cells has drawn increased attention. The deep
learning-based image processing framework is very effective at speeding up the
cell morphological analysis process. Out-of-focus photos taken while capturing
3D cells under several z-slices, however, could negatively impact the deep
learning model. In this work, we created a new algorithm to handle blurry
images while preserving the stacked image quality. Furthermore, we proposed a
unique training architecture that leverages consistency training to help reduce
the bias of the model when dense-slice stacking is applied. Additionally, the
model's stability is increased under the sparse-slice stacking effect by
utilizing the self-training approach. The new blurring stacking technique and
training flow are combined with the suggested architecture and self-training
mechanism to provide an innovative yet easy-to-use framework. Our methods
produced noteworthy experimental outcomes in terms of both quantitative and
qualitative aspects.
