  Different to semantic segmentation, instance segmentation assigns unique
labels to each individual instance of the same class. In this work, we propose
a novel recurrent fully convolutional network architecture for tracking such
instance segmentations over time. The network architecture incorporates
convolutional gated recurrent units (ConvGRU) into a stacked hourglass network
to utilize temporal video information. Furthermore, we train the network with a
novel embedding loss based on cosine similarities, such that the network
predicts unique embeddings for every instance throughout videos. Afterwards,
these embeddings are clustered among subsequent video frames to create the
final tracked instance segmentations. We evaluate the recurrent hourglass
network by segmenting left ventricles in MR videos of the heart, where it
outperforms a network that does not incorporate video information. Furthermore,
we show applicability of the cosine embedding loss for segmenting leaf
instances on still images of plants. Finally, we evaluate the framework for
instance segmentation and tracking on six datasets of the ISBI celltracking
challenge, where it shows state-of-the-art performance.
