  Artificially generated induced pluripotent stem cells (iPSCs) from somatic
cells play an important role for disease modeling and drug screening of
neurodegenerative diseases. Astrocytes differentiated from iPSCs are important
targets to investigate neuronal metabolism. The astrocyte differentiation
progress can be monitored through the variations of morphology observed from
microscopy images at different differentiation stages, then determined by
molecular biology techniques upon maturation. However, the astrocytes usually
``perfectly'' blend into the background and some of them are covered by
interference information (i.e., dead cells, media sediments, and cell debris),
which makes astrocytes difficult to observe. Due to the lack of annotated
datasets, the existing state-of-the-art deep learning approaches cannot be used
to address this issue. In this paper, we introduce a new task named astrocyte
segmentation with a novel dataset, called IAI704, which contains 704 images and
their corresponding pixel-level annotation masks. Moreover, a novel frequency
domain denoising network, named FDNet, is proposed for astrocyte segmentation.
In detail, our FDNet consists of a contextual information fusion module (CIF),
an attention block (AB), and a Fourier transform block (FTB). CIF and AB fuse
multi-scale feature embeddings to localize the astrocytes. FTB transforms
feature embeddings into the frequency domain and conducts a high-pass filter to
eliminate interference information. Experimental results demonstrate the
superiority of our proposed FDNet over the state-of-the-art substitutes in
astrocyte segmentation, shedding insights for iPSC differentiation progress
prediction.
