PDF(Pubmed)
Abstract：
UNASSIGNED: Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologist\'s skill. Automated brain tumor segmentation is of high importance and does not depend on either inter- or intra-observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1W), T2-weighted (T2W), and T1W contrast-enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.
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.
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.
摘要：
■脑肿瘤分割对诊断和治疗计划有很大贡献。手动脑肿瘤描绘是一项耗时且繁琐的任务，并且根据放射科医师的技能而有所不同。自动脑肿瘤分割非常重要，并且不依赖于内部或内部观察。这项研究的目的是从流体衰减反转恢复（FLAIR）自动描绘脑肿瘤，T1加权(T1W),T2加权(T2W),和T1W对比增强（T1ce）磁共振（MR）序列通过深度学习方法，集中于确定单独哪个MR序列或其哪个组合将导致其中的最高精度。
■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。
■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。
