  The goal of unsupervised anomaly segmentation (UAS) is to detect the
pixel-level anomalies unseen during training. It is a promising field in the
medical imaging community, e.g, we can use the model trained with only healthy
data to segment the lesions of rare diseases. Existing methods are mainly based
on Information Bottleneck, whose underlying principle is modeling the
distribution of normal anatomy via learning to compress and recover the healthy
data with a low-dimensional manifold, and then detecting lesions as the outlier
from this learned distribution. However, this dimensionality reduction
inevitably damages the localization information, which is especially essential
for pixel-level anomaly detection. In this paper, to alleviate this issue, we
introduce the semantic space of healthy anatomy in the process of modeling
healthy-data distribution. More precisely, we view the couple of segmentation
and synthesis as a special Autoencoder, and propose a novel cycle translation
framework with a journey of 'image->semantic->image'. Experimental results on
the BraTS and ISLES databases show that the proposed approach achieves
significantly superior performance compared to several prior methods and
segments the anomalies more accurately.
