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。

基于模态内不对称性和模态间依赖性的不完整多模态脑肿瘤分割的增强