  Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy significantly alleviates the
class imbalance issue via running a set of individual deep models for
coarse-to-fine segmentation. Despite its outstanding performance, however, this
method leads to undesired system complexity and also ignores the correlation
among the models. To handle these flaws, we propose a light-weight deep model,
i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better
than MC does, while requiring only one-pass computation. First, OM-Net
integrates the separate segmentation tasks into one deep model, which consists
of shared parameters to learn joint features, as well as task-specific
parameters to learn discriminative features. Second, to more effectively
optimize OM-Net, we take advantage of the correlation among tasks to design
both an online training data transfer strategy and a curriculum learning-based
training strategy. Third, we further propose sharing prediction results between
tasks and design a cross-task guided attention (CGA) module which can
adaptively recalibrate channel-wise feature responses based on the
category-specific statistics. Finally, a simple yet effective post-processing
method is introduced to refine the segmentation results. Extensive experiments
are conducted to demonstrate the effectiveness of the proposed techniques. Most
impressively, we achieve state-of-the-art performance on the BraTS 2015 testing
set and BraTS 2017 online validation set. Using these proposed approaches, we
also won joint third place in the BraTS 2018 challenge among 64 participating
teams. The code is publicly available at
https://github.com/chenhong-zhou/OM-Net.
