  Accurate segmentation of brain tumors from magnetic resonance imaging (MRI)
is clinically relevant in diagnoses, prognoses and surgery treatment, which
requires multiple modalities to provide complementary morphological and
physiopathologic information. However, missing modality commonly occurs due to
image corruption, artifacts, different acquisition protocols or allergies to
certain contrast agents in clinical practice. Though existing efforts
demonstrate the possibility of a unified model for all missing situations, most
of them perform poorly when more than one modality is missing. In this paper,
we propose a novel Adversarial Co-training Network (ACN) to solve this issue,
in which a series of independent yet related models are trained dedicated to
each missing situation with significantly better results. Specifically, ACN
adopts a novel co-training network, which enables a coupled learning process
for both full modality and missing modality to supplement each other's domain
and feature representations, and more importantly, to recover the `missing'
information of absent modalities. Then, two unsupervised modules, i.e., entropy
and knowledge adversarial learning modules are proposed to minimize the domain
gap while enhancing prediction reliability and encouraging the alignment of
latent representations, respectively. We also adapt modality-mutual information
knowledge transfer learning to ACN to retain the rich mutual information among
modalities. Extensive experiments on BraTS2018 dataset show that our proposed
method significantly outperforms all state-of-the-art methods under any missing
situation.
