Models for segmentation of brain lesions in multi-modal MRI are commonly
trained for a specific pathology using a single database with a predefined set
of MRI modalities, determined by a protocol for the specific disease. This work
explores the following open questions: Is it feasible to train a model using
multiple databases that contain varying sets of MRI modalities and annotations
for different brain pathologies? Will this joint learning benefit performance
on the sets of modalities and pathologies available during training? Will it
enable analysis of new databases with different sets of modalities and
pathologies? We develop and compare different methods and show that promising
results can be achieved with appropriate, simple and practical alterations to
the model and training framework. We experiment with 7 databases containing 5
types of brain pathologies and different sets of MRI modalities. Results
demonstrate, for the first time, that joint training on multi-modal MRI
databases with different brain pathologies and sets of modalities is feasible
and offers practical benefits. It enables a single model to segment pathologies
encountered during training in diverse sets of modalities, while facilitating
segmentation of new types of pathologies such as via follow-up fine-tuning. The
insights this study provides into the potential and limitations of this
paradigm should prove useful for guiding future advances in the direction. Code
and pretrained models: this https URL

通过联合训练多模态 MRI 数据库的不同脑病理学和模态集合，能够实现对训练过程中遇到的病理分割和新型病理的分割，并且提供实际的好处，该方法为今后的研究方向提供了有用的指导。