Discretely-constrained deep network for weakly supervised segmentation
This is an incremental improvement for medical image segmentation, addressing efficiency in weakly supervised settings.
The paper tackles weakly supervised medical image segmentation by training a CNN with discrete constraints and regularization priors using ADMM, achieving performance close to full supervision on a cardiac dataset.
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), however this has so far been done within a continuous optimization framework. Yet, various segmentation constraints and regularization can be modeled and optimized more efficiently in a discrete formulation. This paper proposes a method, based on the alternating direction method of multipliers (ADMM) algorithm, to train a CNN with discrete constraints and regularization priors. This method is applied to the segmentation of medical images with weak annotations, where both size constraints and boundary length regularization are enforced. Experiments on a benchmark cardiac segmentation dataset show our method to yield a performance near to full supervision.