A dynamic interactive learning framework for automated 3D medical image
  segmentation
Mu Tian Xiaohui Chen Yi Gao
Many deep learning based automated medical image segmentation systems, in
reality, face difficulties in deployment due to the cost of massive data
annotation and high latency in model iteration. We propose a dynamic
interactive learning framework that addresses these challenges by integrating
interactive segmentation into end-to-end weak supervised learning with
streaming tasks. We develop novel replay and label smoothing schemes that
overcome catastrophic forgetting and improve online learning robustness. For
each image, our multi-round interactive segmentation module simultaneously
optimizes both front-end predictions and deep learning segmenter. In each
round, a 3D "proxy mask" is propagated from sparse user inputs based on image
registration, serving as weak supervision that enable knowledge distillation
from the unknown ground truth. In return, the trained segmenter explicitly
guides next step's user interventions according to a spatial residual map from
consecutive front or back-end predictions. Evaluation on 3D segmentation tasks
(NCI-ISBI2013 and BraTS2015) shows that our framework generates online learning
performances that match offline training benchmark. In addition, with a 62%
reduction in total annotation efforts, our framework produces competitive dice
scores comparing to online and offline learning which equipped with full ground
truth. Furthermore, such a framework, with its flexibility and responsiveness,
could be deployed behind hospital firewall that guarantees data security and
easy maintenance.