  Deep learning-based brain tumor segmentation (BTS) models for multi-modal MRI
images have seen significant advancements in recent years. However, a common
problem in practice is the unavailability of some modalities due to varying
scanning protocols and patient conditions, making segmentation from incomplete
MRI modalities a challenging issue. Previous methods have attempted to address
this by fusing accessible multi-modal features, leveraging attention
mechanisms, and synthesizing missing modalities using generative models.
However, these methods ignore the intrinsic problems of medical image
segmentation, such as the limited availability of training samples,
particularly for cases with tumors. Furthermore, these methods require training
and deploying a specific model for each subset of missing modalities. To
address these issues, we propose a novel approach that enhances the BTS model
from two perspectives. Firstly, we introduce a pre-training stage that
generates a diverse pre-training dataset covering a wide range of different
combinations of tumor shapes and brain anatomy. Secondly, we propose a
post-training stage that enables the model to reconstruct missing modalities in
the prediction results when only partial modalities are available. To achieve
the pre-training stage, we conceptually decouple the MRI image into two parts:
`anatomy' and `tumor'. We pre-train the BTS model using synthesized data
generated from the anatomy and tumor parts across different training samples.
... Extensive experiments demonstrate that our proposed method significantly
improves the performance over the baseline and achieves new state-of-the-art
results on three brain tumor segmentation datasets: BRATS2020, BRATS2018, and
BRATS2015.
