  Automatic medical volume segmentation often lacks clinical accuracy,
necessitating further refinement. In this work, we interactively approach
medical volume segmentation via two decoupled modules:
interaction-to-segmentation and segmentation propagation. Given a medical
volume, a user first segments a slice (or several slices) via the interaction
module and then propagates the segmentation(s) to the remaining slices. The
user may repeat this process multiple times until a sufficiently high volume
segmentation quality is achieved. However, due to the lack of human correction
during propagation, segmentation errors are prone to accumulate in the
intermediate slices and may lead to sub-optimal performance. To alleviate this
issue, we propose a simple yet effective cycle consistency loss that
regularizes an intermediate segmentation by referencing the accurate
segmentation in the starting slice. To this end, we introduce a backward
segmentation path that propagates the intermediate segmentation back to the
starting slice using the same propagation network. With cycle consistency
training, the propagation network is better regularized than in standard
forward-only training approaches. Evaluation results on challenging
AbdomenCT-1K and OAI-ZIB datasets demonstrate the effectiveness of our method.
