  Fluorescence microscopy images play the critical role of capturing spatial or
spatiotemporal information of biomedical processes in life sciences. Their
simple structures and semantics provide unique advantages in elucidating
learning behavior of deep neural networks (DNNs). It is generally assumed that
accurate image annotation is required to train DNNs for accurate image
segmentation. In this study, however, we find that DNNs trained by label images
in which nearly half (49%) of the binary pixel labels are randomly flipped
provide largely the same segmentation performance. This suggests that DNNs
learn high-level structures rather than pixel-level labels per se to segment
fluorescence microscopy images. We refer to these structures as
meta-structures. In support of the existence of the meta-structures, when DNNs
are trained by a series of label images with progressively less meta-structure
information, we find progressive degradation in their segmentation performance.
Motivated by the learning behavior of DNNs trained by random labels and the
characteristics of meta-structures, we propose an unsupervised segmentation
model. Experiments show that it achieves remarkably competitive performance in
comparison to supervised segmentation models.
