UNASSIGNED: The BraTS-2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single-channel input) and any combination thereof (dual- or multi-channel input).
UNASSIGNED: The quantitative assessment of the single-channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual-channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index.
UNASSIGNED: The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.
■BraTS-2020挑战数据集,包含370名受试者,具有四个MR序列和手动描绘的肿瘤面罩,用于训练残差神经网络。针对MR序列中的每一个(单通道输入)及其任何组合(双通道或多通道输入)单独地训练和评估该网络。
■单通道模型的定量评估表明,与具有0.77±0.10Dice指数的对应序列相比,FLAIR序列将产生更高的分割精度。至于考虑双通道模型,具有FLAIR和T2W输入的模型产生0.80±0.10Dice指数,表现出更高的性能。在整个四个MR序列上的联合肿瘤分割产生最高的总体分割精度,具有0.82±0.09Dice指数。
■FLAIRMR序列被认为是在单个MR序列上进行肿瘤分割的最佳选择,而在整个四个MR序列上的联合分割将产生更高的肿瘤描绘精度。