The hippocampus plays a vital role in the diagnosis and treatment of many neurological disorders. Recent years, deep learning technology has made great progress in the field of medical image segmentation, and the performance of related tasks has been constantly refreshed. In this paper, we focus on the hippocampus segmentation task and propose a novel hierarchical feedback chain network. The feedback chain structure unit learns deeper and wider feature representation of each encoder layer through the hierarchical feature aggregation feedback chains, and achieves feature selection and feedback through the feature handover attention module. Then, we embed a global pyramid attention unit between the feature encoder and the decoder to further modify the encoder features, including the pair-wise pyramid attention module for achieving adjacent attention interaction and the global context modeling module for capturing the long-range knowledge. The proposed approach achieves state-of-the-art performance on three publicly available datasets, compared with existing hippocampus segmentation approaches.
翻译:河马坎普斯在诊断和治疗许多神经系统疾病方面发挥着至关重要的作用。 近年来,深层学习技术在医学图像分割领域取得了巨大进步,相关任务的执行情况不断得到更新。 在本文件中,我们侧重于河马坎普斯分割任务,并提出了一个新的等级反馈链网络。反馈链结构单位通过分级特征聚合反馈链,学习每个编码层的更深和更广泛的特征代表,并通过特征传输关注模块实现特征选择和反馈。然后,我们在特征编码器和解码器之间嵌入一个全球金字塔关注单位,以进一步修改编码器特征,包括实现相邻关注互动的对等金字塔关注模块和获取长程知识的全球背景模型。与现有的河马峰分割方法相比,拟议方法在三种公开的数据集上取得了最新表现。