  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.
