Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Each such model cannot segment the disease using data with a different set of MRI modalities, nor can it segment any other type of disease. Moreover, this training paradigm does not allow a model to benefit from learning from heterogeneous databases that may contain scans and segmentation labels for different types of brain pathologies and diverse sets of MRI modalities. Is it feasible to use Federated Learning (FL) for training a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that such FL framework can train a single model that is shown to be very promising in segmenting all disease types seen during training. Importantly, it is able to segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, feasibility and effectiveness of using FL to train a single segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code will be made available at: https://github.com/FelixWag/FL-MultiDisease-MRI

翻译：针对MRI脑部病灶分割模型通常针对特定疾病开发，并在预定义MRI模态数据集上进行训练。此类模型既无法利用不同MRI模态组合的数据分割该疾病，也无法分割其他类型的脑部疾病。此外，这种训练范式使模型无法从包含不同类型脑部病理学扫描数据及多样化MRI模态的异构数据库中获益。是否可能通过联邦学习（FL）在包含不同脑部病理学扫描数据及多样化MRI模态的客户端数据库上训练单一模型？我们通过对模型架构与训练策略进行适当、简洁且实用的改进，展示了具有前景的研究成果：设计覆盖所有客户端可用模态的输入通道模型，采用随机模态丢弃训练方法，并探索特征归一化方法的影响。在包含5种不同疾病的7个脑部MRI数据库上的评估表明，该联邦学习框架能够训练出单一模型，在分割训练期间所见的所有疾病类型方面展现出显著潜力。尤为重要的是，该模型能够在新数据库中分割这些疾病，即使这些数据库包含的模态组合与训练客户端完全不同。这些成果首次证明了利用联邦学习在具有多样化脑部疾病和MRI模态的分散数据上训练单一分割模型的可行性与有效性，这是利用异构现实世界数据库的关键步骤。代码将在以下地址公开：https://github.com/FelixWag/FL-MultiDisease-MRI