The morphological analysis of hippocampus is vital to various neurological studies including brain disorders and brain anatomy. To assist doctors in analyzing the shape and volume of the hippocampus, an accurate and automatic hippocampus segmentation method is highly demanded in the clinical practice. Given that fully convolutional networks (FCNs) have made significant contributions in biomedical image segmentation applications, we propose a notably discriminative feature network based on a hierarchical attention mechanism in hippocampal segmentation. First, considering the problem that the hippocampus is a rather small part in MR images, we design a context-aware high-level feature extraction module (CHFEM) to extract high-level features of scale invariance in the encoder stage. Further, we introduce a hierarchical attention mechanism into our segmentation framework. The mechanism is divided into three parts: a low-level feature spatial attention module (LFSAM) is developed to learn the spatial relationship between different pixels on each channel in the low-level stage of the encoder, a high-level feature channel attention module (HFCAM) is to model the semantic information relationship on different channel images in the high-level stage of the encoder, and a cross-connected attention module (CCAM) is designed in the decoder part to further suppress the noisy boundaries of hippocampus and simultaneously utilize the attentional low-level features from the encoder to better guide the high-level hippocampus edge segmentation in the decoder phase. The proposed approach achieves outstanding performance on the ADNI dataset and the Decathlon dataset compared with other semantic segmentation models and existing hippocampal segmentation approaches. Source code is available at https://github.com/LannyShi/Hippocampal-segmentation.

中文翻译：

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基于分层注意机制的判别特征网络语义海马分割

海马体的形态分析对于包括脑部疾病和脑部解剖学在内的各种神经学研究至关重要。为了辅助医生分析海马体的形状和体积，临床上迫切需要一种准确、自动的海马体分割方法。鉴于全卷积网络 (FCN) 在生物医学图像分割应用中做出了重大贡献，我们提出了一种基于海马分割中的分层注意机制的显着区分特征网络。首先，考虑到海马体在 MR 图像中所占比例较小的问题，我们设计了一个上下文感知高级特征提取模块（CHFEM）来在编码器阶段提取尺度不变性的高级特征。更远，我们在分割框架中引入了分层注意力机制。该机制分为三个部分：一个低级特征空间注意力模块（LFSAM）用于在编码器的低级阶段学习每个通道上不同像素之间的空间关系，一个高级特征通道注意力模块(HFCAM) 是在编码器的高层阶段对不同通道图像上的语义信息关系进行建模，在解码器部分设计了一个交叉连接的注意力模块 (CCAM)，以进一步抑制海马体的噪声边界，同时利用来自编码器的注意力低级特征在解码器阶段更好地指导高级海马边缘分割。与其他语义分割模型和现有的海马分割方法相比，所提出的方法在 ADNI 数据集和 Decathlon 数据集上取得了出色的性能。源代码可在 https://github.com/LannyShi/Hippocampal-segmentation 获得。