【摘要】 在婴幼儿脑组织分割领域中,婴幼儿脑组织存在对比度低、灰度不均匀等问题,这些问题导致现有方法的精度仍然达不到满意的结果.因此,本文提出了一种基于三维U-Net网络的脑部核磁共振图像组织分割方法,融合注意力机制模块和金字塔结构模块,可以更好地在不同的层次和位置提供模型信息,图像的上下文信息得到充分的应用以降低图像信息损失,同样还可以挖掘通道映射之间的相互依赖关系和特征映射,提高特定语义的特征表示.在Iseg2017数据集中所提出算法的WM(白质),GM(灰质)的DICE指标结果与此前最优结果相比提高了0.7%,0.7%,CSF(脑脊液)则具有可对比性.在Iseg2019跨数据集挑战的评估当中,WM,GM的分割结果在DICE,ASD两个指标均取得了第一名,CSF的指标获得第二名.更多还原

【Abstract】 In the field of infant brain tissue segmentation,infant brain tissue has problems such as low contrast and uneven gray scale. These problems lead to the unsatisfied accuracy of the existing methods. A brain MRI image tissue segmentation method was proposed based on a threedimensional U-Net network(3D U-Net),which combines the attention mechanism module and the pyramid structure module,to better provide model information at different levels and positions.The contextual information of the image is fully applied to reduce the loss of image information. It can also mine the interdependence and feature mapping between channel mappings to improve the feature representation of specific semantics. The DICE index results of WM(white matter) and GM(gray matter) of the algorithm proposed in the Iseg2017 dataset have increased by 0. 7%compared with the previous optimal results,and the CSF(cerebrospinal fluid) index is with comparability. In the evaluation of the Iseg 2019 cross-dataset challenge,the segmentation results of WM,GM in DICE ratio and ASD achieved first place,while the CSF index won the second place.更多还原