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.

基于深度学习的脑肿瘤分割（BTS）模型在多模态 MRI 图像中取得了重要进展。然而，不完整的 MRI 模态造成分割成为一个具有挑战性的问题。为了解决这些问题，我们提出了一种新颖的方法，从两个方面增强 BTS 模型。首先，我们引入了预训练阶段，生成一个广泛覆盖肿瘤形状和大脑解剖不同组合的多样数据集。其次，我们提出了后训练阶段，使模型能够在只有部分模态可用时重建缺失的模态。通过大量实验证明，我们的方法显著改善了基线性能，并在三个脑肿瘤分割数据集（BRATS2020、BRATS2018 和 BRATS2015）上取得了新的最先进成果。