  Instance object segmentation and tracking provide comprehensive
quantification of objects across microscope videos. The recent single-stage
pixel-embedding based deep learning approach has shown its superior performance
compared with "segment-then-associate" two-stage solutions. However, one major
limitation of applying a supervised pixel-embedding based method to microscope
videos is the resource-intensive manual labeling, which involves tracing
hundreds of overlapped objects with their temporal associations across video
frames. Inspired by the recent generative adversarial network (GAN) based
annotation-free image segmentation, we propose a novel annotation-free
synthetic instance segmentation and tracking (ASIST) algorithm for analyzing
microscope videos of sub-cellular microvilli. The contributions of this paper
are three-fold: (1) proposing a new annotation-free video analysis paradigm is
proposed. (2) aggregating the embedding based instance segmentation and
tracking with annotation-free synthetic learning as a holistic framework; and
(3) to the best of our knowledge, this is first study to investigate microvilli
instance segmentation and tracking using embedding based deep learning. From
the experimental results, the proposed annotation-free method achieved superior
performance compared with supervised learning.
